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overreact is a library and a command-line tool for building and
analyzing homogeneous microkinetic models from first-principles
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Wavefunction\n and\n Density Functional\n theories.\n For instance, the three-line example code above calculates the rate of methyl rotation in ethane (at\n B97-3c).\n (Rather surprisingly, the error found is less than 2%\n when compared to available experimental results.)\n overreact uses precise thermochemical partition funtions, tunneling\ncorrections and data is parsed directly from computational chemistry\noutput files thanks to overreact is a Python package, so you can easily install it with\n See the\ninstallation guide\nfor more details. \ud83d\ude80 Where to go from here? Take a look at the\n short introduction.\n Or see\n below\n for more guidance. If you use overreact in your research, please cite: F. S. S. Schneider and G. F. Caramori. _geem-lab/overreact: a tool for\n creating and analyzing microkinetic models built from computational chemistry\n data, v1.0.2_. 2021.\n DOI:10.5281/zenodo.5730603.\n Freely available at: <https://github.com/geem-lab/overreact>. Here's the reference in BibTeX format: \u270f\ufe0f A paper describing overreact is currently being prepared. When it\n is published, the above BibTeX entry will be updated. overreact is open-source, released under the permissive MIT license. See\nthe LICENSE agreement. This project was developed at the GEEM lab\n(Federal University of Santa Catarina, Brazil), and was\npartially funded by the\nBrazilian National Council for Scientific and Technological Development (CNPq),\ngrant number 140485/2017-1. This module contains the high-level application programming interface. If you intend to use overreact as a library in a project, you should\nprobably start here. Obtain reaction rate constants for a given reaction scheme. Some symbols are accepted as alternatives in \"scale\": \"M-1\", \"ml\" and\n\"torr-1\" are understood as \"l mol-1\", \"cm3\" and \"mmHg-1\", respectively. Below is an example of an estimate for the rate of methyl rotation in\nethane (a trivial attempt to reproduce\nScience, 2006, 313, 5795, 1951-1955).\nHow many turns it does per second? The calculated value is off by less than 2% from the experimental value\n(\\( \\frac{1}{12 \\times 10^{-12}} \\text{s}^{-1} = 8.33 \\times 10^{10} \\text{s}^{-1} \\)).\nWe use Eckart tunneling by default, but see the effect of changing it\nabove. The units of the returned reaction rate constants can be selected for\nnon-unimolecular processes. The following is an attempt to reproduce\nJ Atmos Chem, 1996 23, 37\u201349 for\nthe reaction of proton-withdrawal by a chloride radical from the methane\nmolecule\n\\( \\ce{CH4 + \\cdot Cl -> [H3C\\cdots H\\cdots Cl]^\\ddagger -> H3C\\cdot + HCl} \\): (By the way, according to the Jet Propulsion Laboratory,\nPublication No. 19-5,\nthe experimental reaction rate constant for this reaction is\n\\( 1.0 \\times 10^{-13} \\text{cm}^3 \\text{particle}^{-1} \\text{s}^{-1} \\).) The returned units are \"M-1 s-1\" by default: You can also turn the tunneling correction off by using the string \"none\": You can set a simple energy bias, either as a constant or compound-wise: Obtain tunneling transmission coefficients at a given temperature. One tunneling transmission coefficient is calculated for each reaction. If\na reaction lacks a transition state (i.e., a half-equilibrium reaction),\nits transmission coefficient is set to unity. Below is an example of an estimate of how much quantum tunneling\ncontributes to the rate of methyl rotation in ethane (see\nScience, 2006, 313, 5795, 1951-1955\nfor some interesting experimental data on this reaction). You can calculate each piece of the reaction rate constant by hand,\nif you want. Just make sure that you don't calculate the tunneling\ncoefficient twice: Obtain free energies for compounds at a given temperature and pressure. You can consider all compounds as solvated if you want: You can set a simple energy bias, either as a constant or compound-wise: Obtain entropies for compounds at a given temperature and pressure. You can consider all compounds as solvated if you want: Obtain enthalpies for compounds at a given temperature. The enthalpies at absolute zero can easily be obtained (this is used,\ne.g., in the calculation of the Eckart tunneling coefficient, see\n Obtain internal energies for compounds at a given temperature. Module dedicated to classifying molecules into point groups. Determine point group of structure. Return rotational symmetry number for point group. This function has a set of the most common point groups precomputed, but is\nable to calculate the symmetry number if it is not found in known tables.\nMost precomputed values are from\nTheor Chem Account 2007 118, 813\u2013826. Module dedicated to parsing and modeling of chemical reaction networks. A descriptor of a chemical reaction network. Mostly likely, this comes from a parsed input file.\nSee Create new instance of Scheme(compounds, reactions, is_half_equilibrium, A, B) A descriptor of compounds. A descriptor of reactions. An indicator of whether a reaction is half-equilibrium. A matrix of stoichiometric coefficients between reactants and products. A matrix of stoichiometric coefficients between reactants and transition states. Parse a kinetic model as a chemical reaction scheme. This is an essential part of the parsing process.\nSee The model description should comply with the mini-language for systems of\nreactions. A semi-formal definition of the grammar in\nBackus\u2013Naur form\nis given below: Blank lines and comments (starting with What follows is a rather long tour over the parsing process and its\noutput in general. You can skip it if you are not interested in the\ndetails. The reaction above is a direct one (observe that comments are ignored). The\nreturned object has the following attributes: The same reaction can be specified in reverse order: Equilibria produce twice as many direct reactions, while the $B$ matrix\ndefines an energy relationship for only one of each pair: Adding twice the same reaction results in a single reaction being added.\nThis of course also works with equilibria (extra whitespaces are ignored): Transition states are specified with a special symbol at the end (either\n This gives the same result as above: It is possible to define a reaction whose product is the same as the\nreactant. This is found in isomerization processes (e.g., ammonia\ninversion or the methyl rotation in ethane): As such, a column full of zeros in the $A$ matrix corresponds to a reaction\nwith zero net change. As can be seen, overreact allows for very general\nmodels. An interesting feature is that a single transition state can link\nmany different compounds (whether it is useful is a matter of debate): The following is a borderline case but both reactions should be considered\ndifferent since they define different processes: The following is correct behavior. In fact, the reactions are badly\ndefined: if more than one transition state are chained, the following\nhappens, which is correct since it's the most physically plausible model\nthat can be extracted. It can be seen as a feature that the product B is\nignored and not the reactant A, since the user would easily see the mistake\nin graphs of concentration over time (the alternative would be no\nreaction happening at all, which is rather cryptic to debug). In any case, it's not clear how a reaction barrier be defined in such a\ncase. If you have a use case, don't hesitate to\nopen an issue, we'll be\nhappy to hear from you. Basic I/O operations (such as reading source input files). Parse either a source or model input file, whichever is available. A source input file (also known as a You probably won't need to use model input files directly, they are\nautomatically created based on source input files.\nTake a look at our guide on how to write an source input file. This function attempts to parse a model input file if available. If not, a source\ninput file is parsed and a model input file is generated from it. Extensions are\nguessed if none given (i.e., if only the base name given). Some examples of how overreact \"sees\" your data below \ud83d\ude04: Module dedicated to the calculation of reaction rate constants. Calculate a reaction rate constant. This function uses the Eyring-Evans-Polanyi equation\n from transition state\ntheory : $$k(T) = \\frac{k_\\text{B} T}{h} K^\\ddagger\n = \\frac{k_\\text{B} T}{h}\n \\exp\\left(-\\frac{\\Delta^\\ddagger G^\\circ}{R T}\\right)$$ where \\( h \\) is Planck's constant, \\( k_\\text{B} \\) is Boltzmann's\nconstant and \\( T \\) is the absolute temperature. This function uses The following are examples from\nThermochemistry in Gaussian, in which the\nkinetic isotope effect of a bimolecular reaction is analyzed: It is well known that, at room temperature, if you \"decrease\" a reaction\nbarrier by 1.4 kcal/mol, the reaction becomes around ten times faster: A similar relationship is found for a twofold increase in speed and a\n0.4 kcal/mol decrease in the reaction barrier (again, at room\ntemperature): Module dedicated to the time simulation of reaction models. Here are functions that calculate reaction rates as well, which is needed for\nthe time simulations. Simulate a reaction scheme from its rate function. This function provides two functions that calculate the concentrations and\nthe rates of formation at any point in time for any compound. It does that\nby solving an initial value problem (IVP) through scipy's A toy simulation can be performed in just two lines: The Both Generate a rate function that models a reaction scheme. The returned function is suited to be used by ODE solvers such as\n If available, JAX is used for JIT compilation. This will make The actually used reaction rate constants can be inspected with the If JAX is available, the Jacobian function will be available as\n Generate an alternative scheme with some concentrations fixed. This function returns data that allow the microkinetic simulation of a\nreaction network under constraints, namely when some compounds have fixed\nconcentrations. This works by 1. removing all references to the fixed\ncompounds and by 2. properly multiplying the reaction rate constants by\nthe respective concentrations. Keep in mind that when a compound get its concentration fixed, the\nreaction scheme no longer conserves matter. You can think of it as\nreacting close to an infinite source of the compound, but it accumulates\nin the milleu at the given concentration. Equilibria under a specific pH can be easily modeled: It is also possible to model the fixed activity of a solvent, for\ninstance: Multiple reactions work fine, see both examples below: Multiple fixed compounds also work fine: This function is a no-op if Module dedicated to the calculation of thermodynamic properties. Calculate an equilibrium constant from a reaction Gibbs free energy. This function uses the usual relationship between reaction Gibbs energy\nand equilibrium constant\n: $$K(T) = \\exp\\left(-\\frac{\\Delta_\\text{r} G^\\circ}{R T}\\right)$$ If If you want to calculate a solution equilibrium constant from gas phase data, set\n The following is an example from the\nLibreTexts Chemistry Library.\nConsider the following equilibrium: $$\\ce{2 SO2(g) + O2(g) <=> 2 SO3(g)}$$ with concentrations \\( c_{\\ce{SO2(g)}} = 0.2 M \\),\n\\( c_{\\ce{O2(g)}} = 0.5 M \\) and \\( c_{\\ce{SO3(g)}} = 0.7 M \\) (room\ntemperature). Its \\( K_c \\) is given by: You could use By giving a (As expected, it makes sense for gases to favor the most entropic side of\nthe equilibrium.) The example above clearly used \"solution-based\" data\n(our \\( K_c \\) was calculated using molar quantities, which means\nreference volumes of one liter). You could convert it to gas phase data\nto get the same result, by changing the reference state (in this case,\nfrom one molar to one atmosphere using Having gas phase information, the inverse path can be taken just by\ninverting the sign of The following example is from\nWikipedia.\nThe reactions are two copper complex forming equilibria with two\ndifferent ligands. Since this reaction takes place in solution, it is the\nsolution standard Gibbs reaction free energy that is given: The above are thus \\( \\log_{10}(K_c) \\). Since we are talking about a\nmono- and a bidendate ligands, the You can easily check that the above values match the values given\nhere. Calculate an additive entropy correction to a change in reference states. $$\\Delta G_\\text{corr} =\n R T \\ln \\left( \\frac{\\chi_\\text{new}}{\\chi_\\text{old}} \\right)$$ The value returned can be directly multiplied by temperature and summed to\nthe old reference free energies to obtain free energies with respect to a\nnew reference. See notes below. For instance, the concentration correction to Gibbs free energy for a\ngas-to-liquid standard state change is simply\n(\\( c^\\circ = \\frac{\\text{1 atm}}{R T} \\)), $$\\Delta G_\\text{conc} =\n R T \\ln \\left( \\frac{\\text{1 M}}{c^\\circ} \\right)$$ This function can be used to add any entropy correction in the form above.\nThe only drawback is that, sometimes, those corrections are written with a\nminus sign in front of them (this implies switching the roles of\n By default, the correction returns a change in concentration from the gas\nphase standard concentration to the solvated-state standard concentration: But this function can also be used to adjust symmetry effects from C1\ncalculations (symmetry number equals to one). For D7h, for instance, the\nsymmetry number is 14: Module dedicated to quantum tunneling approximations. Calculate the Eckart correction to quantum tunneling. References are\nJ. Phys. Chem. 1962, 66, 3, 532\u2013533\nand\nJ. Res. Natl. Inst. Stand. Technol., 1981, 86, 357. If no backward barrier is given, a symmetric Eckart potential is assumed: Calculate the Wigner correction to quantum tunneling. overreact is a library and a command-line tool for building and\nanalyzing homogeneous microkinetic models from first-principles\ncalculations: \n Microkinetic modeling is a technique used to predict the outcome\n of complex chemical reactions.\n It can be used\n to investigate the catalytic transformations\n of molecules.\n overreact makes it easy to create\n and analyze microkinetic models built\n from computational chemistry data.\n \n We use the term first-principles calculations to refer to\n calculations performed using quantum chemical modern methods such as\n Wavefunction\n and\n Density Functional\n theories.\n For instance, the three-line example code above calculates the rate of methyl rotation in ethane (at\n B97-3c).\n (Rather surprisingly, the error found is less than 2%\n when compared to available experimental results.)\n overreact uses precise thermochemical partition funtions, tunneling\ncorrections and data is parsed directly from computational chemistry\noutput files thanks to overreact is a Python package, so you can easily install it with\n See the\ninstallation guide\nfor more details. \ud83d\ude80 Where to go from here? Take a look at the\n short introduction.\n Or see\n below\n for more guidance. If you use overreact in your research, please cite: F. S. S. Schneider and G. F. Caramori. _geem-lab/overreact: a tool for\n creating and analyzing microkinetic models built from computational chemistry\n data, v1.0.2_. 2021.\n DOI:10.5281/zenodo.5730603.\n Freely available at: <https://github.com/geem-lab/overreact>. Here's the reference in BibTeX format: \u270f\ufe0f A paper describing overreact is currently being prepared. When it\n is published, the above BibTeX entry will be updated. overreact is open-source, released under the permissive MIT license. See\nthe LICENSE agreement. This project was developed at the GEEM lab\n(Federal University of Santa Catarina, Brazil), and was\npartially funded by the\nBrazilian National Council for Scientific and Technological Development (CNPq),\ngrant number 140485/2017-1. This module contains the high-level application programming interface. If you intend to use overreact as a library in a project, you should\nprobably start here. Obtain reaction rate constants for a given reaction scheme. Some symbols are accepted as alternatives in \"scale\": \"M-1\", \"ml\" and\n\"torr-1\" are understood as \"l mol-1\", \"cm3\" and \"mmHg-1\", respectively. Below is an example of an estimate for the rate of methyl rotation in\nethane (a trivial attempt to reproduce\nScience, 2006, 313, 5795, 1951-1955).\nHow many turns it does per second? The calculated value is off by less than 2% from the experimental value\n(\\( \\frac{1}{12 \\times 10^{-12}} \\text{s}^{-1} = 8.33 \\times 10^{10} \\text{s}^{-1} \\)).\nWe use Eckart tunneling by default, but see the effect of changing it\nabove. The units of the returned reaction rate constants can be selected for\nnon-unimolecular processes. The following is an attempt to reproduce\nJ Atmos Chem, 1996 23, 37\u201349 for\nthe reaction of proton-withdrawal by a chloride radical from the methane\nmolecule\n\\( \\ce{CH4 + \\cdot Cl -> [H3C\\cdots H\\cdots Cl]^\\ddagger -> H3C\\cdot + HCl} \\): (By the way, according to the Jet Propulsion Laboratory,\nPublication No. 19-5,\nthe experimental reaction rate constant for this reaction is\n\\( 1.0 \\times 10^{-13} \\text{cm}^3 \\text{particle}^{-1} \\text{s}^{-1} \\).) The returned units are \"M-1 s-1\" by default: You can also turn the tunneling correction off by using the string \"none\": You can set a simple energy bias, either as a constant or compound-wise: Obtain tunneling transmission coefficients at a given temperature. One tunneling transmission coefficient is calculated for each reaction. If\na reaction lacks a transition state (i.e., a half-equilibrium reaction),\nits transmission coefficient is set to unity. Below is an example of an estimate of how much quantum tunneling\ncontributes to the rate of methyl rotation in ethane (see\nScience, 2006, 313, 5795, 1951-1955\nfor some interesting experimental data on this reaction). You can calculate each piece of the reaction rate constant by hand,\nif you want. Just make sure that you don't calculate the tunneling\ncoefficient twice: Obtain free energies for compounds at a given temperature and pressure. You can consider all compounds as solvated if you want: You can set a simple energy bias, either as a constant or compound-wise: Obtain entropies for compounds at a given temperature and pressure. You can consider all compounds as solvated if you want: Obtain enthalpies for compounds at a given temperature. The enthalpies at absolute zero can easily be obtained (this is used,\ne.g., in the calculation of the Eckart tunneling coefficient, see\n Obtain internal energies for compounds at a given temperature. Module dedicated to classifying molecules into point groups. Determine point group of structure. Return rotational symmetry number for point group. This function has a set of the most common point groups precomputed, but is\nable to calculate the symmetry number if it is not found in known tables.\nMost precomputed values are from\nTheor Chem Account 2007 118, 813\u2013826. Module dedicated to parsing and modeling of chemical reaction networks. A descriptor of a chemical reaction network. Mostly likely, this comes from a parsed input file.\nSee Create new instance of Scheme(compounds, reactions, is_half_equilibrium, A, B) A descriptor of compounds. A descriptor of reactions. An indicator of whether a reaction is half-equilibrium. A matrix of stoichiometric coefficients between reactants and products. A matrix of stoichiometric coefficients between reactants and transition states. Parse a kinetic model as a chemical reaction scheme. This is an essential part of the parsing process.\nSee The model description should comply with the mini-language for systems of\nreactions. A semi-formal definition of the grammar in\nBackus\u2013Naur form\nis given below: Blank lines and comments (starting with What follows is a rather long tour over the parsing process and its\noutput in general. You can skip it if you are not interested in the\ndetails. The reaction above is a direct one (observe that comments are ignored). The\nreturned object has the following attributes: The same reaction can be specified in reverse order: Equilibria produce twice as many direct reactions, while the $B$ matrix\ndefines an energy relationship for only one of each pair: Adding twice the same reaction results in a single reaction being added.\nThis of course also works with equilibria (extra whitespaces are ignored): Transition states are specified with a special symbol at the end (either\n This gives the same result as above: It is possible to define a reaction whose product is the same as the\nreactant. This is found in isomerization processes (e.g., ammonia\ninversion or the methyl rotation in ethane): As such, a column full of zeros in the $A$ matrix corresponds to a reaction\nwith zero net change. As can be seen, overreact allows for very general\nmodels. An interesting feature is that a single transition state can link\nmany different compounds (whether it is useful is a matter of debate): The following is a borderline case but both reactions should be considered\ndifferent since they define different processes: The following is correct behavior. In fact, the reactions are badly\ndefined: if more than one transition state are chained, the following\nhappens, which is correct since it's the most physically plausible model\nthat can be extracted. It can be seen as a feature that the product B is\nignored and not the reactant A, since the user would easily see the mistake\nin graphs of concentration over time (the alternative would be no\nreaction happening at all, which is rather cryptic to debug). In any case, it's not clear how a reaction barrier be defined in such a\ncase. If you have a use case, don't hesitate to\nopen an issue, we'll be\nhappy to hear from you. Basic I/O operations (such as reading source input files). Parse either a source or model input file, whichever is available. A source input file (also known as a You probably won't need to use model input files directly, they are\nautomatically created based on source input files.\nTake a look at our guide on how to write an source input file. This function attempts to parse a model input file if available. If not, a source\ninput file is parsed and a model input file is generated from it. Extensions are\nguessed if none given (i.e., if only the base name given). Some examples of how overreact \"sees\" your data below \ud83d\ude04: Module dedicated to the calculation of reaction rate constants. Calculate a reaction rate constant. This function uses the Eyring-Evans-Polanyi equation\n from transition state\ntheory : $$k(T) = \\frac{k_\\text{B} T}{h} K^\\ddagger\n = \\frac{k_\\text{B} T}{h}\n \\exp\\left(-\\frac{\\Delta^\\ddagger G^\\circ}{R T}\\right)$$ where \\( h \\) is Planck's constant, \\( k_\\text{B} \\) is Boltzmann's\nconstant and \\( T \\) is the absolute temperature. This function uses The following are examples from\nThermochemistry in Gaussian, in which the\nkinetic isotope effect of a bimolecular reaction is analyzed: It is well known that, at room temperature, if you \"decrease\" a reaction\nbarrier by 1.4 kcal/mol, the reaction becomes around ten times faster: A similar relationship is found for a twofold increase in speed and a\n0.4 kcal/mol decrease in the reaction barrier (again, at room\ntemperature): Module dedicated to the time simulation of reaction models. Here are functions that calculate reaction rates as well, which is needed for\nthe time simulations. Simulate a reaction scheme from its rate function. This function provides two functions that calculate the concentrations and\nthe rates of formation at any point in time for any compound. It does that\nby solving an initial value problem (IVP) through scipy's A toy simulation can be performed in just two lines: The Both Generate a rate function that models a reaction scheme. The returned function is suited to be used by ODE solvers such as\n If available, JAX is used for JIT compilation. This will make The actually used reaction rate constants can be inspected with the If JAX is available, the Jacobian function will be available as\n Generate an alternative scheme with some concentrations fixed. This function returns data that allow the microkinetic simulation of a\nreaction network under constraints, namely when some compounds have fixed\nconcentrations. This works by 1. removing all references to the fixed\ncompounds and by 2. properly multiplying the reaction rate constants by\nthe respective concentrations. Keep in mind that when a compound get its concentration fixed, the\nreaction scheme no longer conserves matter. You can think of it as\nreacting close to an infinite source of the compound, but it accumulates\nin the milleu at the given concentration. Equilibria under a specific pH can be easily modeled: It is also possible to model the fixed activity of a solvent, for\ninstance: Multiple reactions work fine, see both examples below: Multiple fixed compounds also work fine: This function is a no-op if Module dedicated to the calculation of thermodynamic properties. Calculate an equilibrium constant from a reaction Gibbs free energy. This function uses the usual relationship between reaction Gibbs energy\nand equilibrium constant\n: $$K(T) = \\exp\\left(-\\frac{\\Delta_\\text{r} G^\\circ}{R T}\\right)$$ If If you want to calculate a solution equilibrium constant from gas phase data, set\n The following is an example from the\nLibreTexts Chemistry Library.\nConsider the following equilibrium: $$\\ce{2 SO2(g) + O2(g) <=> 2 SO3(g)}$$ with concentrations \\( c_{\\ce{SO2(g)}} = 0.2 M \\),\n\\( c_{\\ce{O2(g)}} = 0.5 M \\) and \\( c_{\\ce{SO3(g)}} = 0.7 M \\) (room\ntemperature). Its \\( K_c \\) is given by: You could use By giving a (As expected, it makes sense for gases to favor the most entropic side of\nthe equilibrium.) The example above clearly used \"solution-based\" data\n(our \\( K_c \\) was calculated using molar quantities, which means\nreference volumes of one liter). You could convert it to gas phase data\nto get the same result, by changing the reference state (in this case,\nfrom one molar to one atmosphere using Having gas phase information, the inverse path can be taken just by\ninverting the sign of The following example is from\nWikipedia.\nThe reactions are two copper complex forming equilibria with two\ndifferent ligands. Since this reaction takes place in solution, it is the\nsolution standard Gibbs reaction free energy that is given: The above are thus \\( \\log_{10}(K_c) \\). Since we are talking about a\nmono- and a bidendate ligands, the You can easily check that the above values match the values given\nhere. Calculate an additive entropy correction to a change in reference states. $$\\Delta G_\\text{corr} =\n R T \\ln \\left( \\frac{\\chi_\\text{new}}{\\chi_\\text{old}} \\right)$$ The value returned can be directly multiplied by temperature and summed to\nthe old reference free energies to obtain free energies with respect to a\nnew reference. See notes below. For instance, the concentration correction to Gibbs free energy for a\ngas-to-liquid standard state change is simply\n(\\( c^\\circ = \\frac{\\text{1 atm}}{R T} \\)), $$\\Delta G_\\text{conc} =\n R T \\ln \\left( \\frac{\\text{1 M}}{c^\\circ} \\right)$$ This function can be used to add any entropy correction in the form above.\nThe only drawback is that, sometimes, those corrections are written with a\nminus sign in front of them (this implies switching the roles of\n By default, the correction returns a change in concentration from the gas\nphase standard concentration to the solvated-state standard concentration: But this function can also be used to adjust symmetry effects from C1\ncalculations (symmetry number equals to one). For D7h, for instance, the\nsymmetry number is 14: Module dedicated to quantum tunneling approximations. Calculate the Eckart correction to quantum tunneling. References are\nJ. Phys. Chem. 1962, 66, 3, 532\u2013533\nand\nJ. Res. Natl. Inst. Stand. Technol., 1981, 86, 357. If no backward barrier is given, a symmetric Eckart potential is assumed: Calculate the Wigner correction to quantum tunneling.\n\n[](https://pypi.org/project/overreact/)\n[](https://pypi.org/project/overreact/)\n[](https://github.com/geem-lab/overreact/actions/workflows/python-package.yml)\n[](https://codecov.io/gh/geem-lab/overreact)\n[](https://github.com/geem-lab/overreact/blob/main/LICENSE)\n\n[](https://geem-lab.github.io/overreact-guide/)\n[](https://github.com/geem-lab/overreact/discussions)\n[](https://github.com/geem-lab/overreact/issues)\n\n[](https://pepy.tech/project/overreact)\n[](https://pepy.tech/project/overreact)\n\n[](https://zenodo.org/badge/latestdoi/214332027)\n\n[](https://github.com/geem-lab/overreact#funding)\n\n\n\n\n \n\n![\"overreact\"](\"https://raw.githubusercontent.com/geem-lab/overreact-guide/master/logo.png\"){kind=logo}
\n#
\n\n\n\n\n\nIn [1]: from overreact import api\n\nIn [2]: api.get_k("S -> E\u2021 -> S",\n ...: {"S": "data/ethane/B97-3c/staggered.out",\n ...: "E\u2021": "data/ethane/B97-3c/eclipsed.out"})\nOut[2]: array([8.16880917e+10])\n\n \n \ud83e\udd14 What is microkinetic modeling?\n
\n \n \n \ud83e\uddd0 What do you mean by first-principles calculations?\n
\n cclib (see the\nlist of its supported programs).Installation
\n\npip:\n\n$ pip install "overreact[cli,fast]"\n\n
\n\nCiting overreact
\n\n\n
\n\n\n\n@misc{overreact2021,\n howpublished = {\\url{https://github.com/geem-lab/overreact}}\n year = {2021},\n author = {Schneider, F. S. S. and Caramori, G. F.},\n title = {\n \\textbf{geem-lab/overreact}: a tool for creating and analyzing\n microkinetic models built from computational chemistry data, v1.0.2\n },\n doi = {10.5281/zenodo.5730603},\n url = {https://zenodo.org/record/5730603},\n publisher = {Zenodo},\n copyright = {Open Access}\n}\n\n
\n\nLicense
\n\nFunding
\n\nParameters
\n\n\n
\n\noverreact.io.parse_model.overreact.io.parse_model.delta_moles for\noverreact.thermo.equilibrium_constant, which effectively calculates a solution\nequilibrium constant between reactants and the transition state for\ngas phase data. You should set this to None if your free energies\nwere already adjusted for solution Gibbs free energies.Returns
\n\n\n
\n\nNotes
\n\nExamples
\n\n\n\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> get_k(model.scheme, model.compounds)\narray([8.16e+10])\n>>> get_k(model.scheme, model.compounds, qrrho=(False, True))\narray([8.24968117e+10])\n>>> get_k(model.scheme, model.compounds, qrrho=False)\narray([8.26909266e+10])\n>>> get_k(model.scheme, model.compounds, tunneling="wigner")\narray([7.99e+10])\n>>> get_k(model.scheme, model.compounds, tunneling=None)\narray([7.35e+10])\n\n\n>>> model = rx.parse_model("data/tanaka1996/UMP2/cc-pVTZ/model.jk")\n>>> get_k(model.scheme, model.compounds, temperature=300,\n... scale="cm3 particle-1 s-1")\narray([9.60e-14])\n\n\n>>> get_k(model.scheme, model.compounds) \\\n... == get_k(model.scheme, model.compounds, scale="l mol-1 s-1")\narray([ True])\n\n\n>>> get_k(model.scheme, model.compounds, tunneling="none") \\\n... == get_k(model.scheme, model.compounds, tunneling=None)\narray([ True])\n\n", "signature": "(\n scheme: overreact.core.Scheme,\n compounds: Optional[dict] = None,\n bias: float = 0.0,\n tunneling: str = 'eckart',\n qrrho: Union[bool, tuple[bool, bool]] = True,\n scale: str = 'l mol-1 s-1',\n temperature: float = 298.15,\n pressure: float = 101325.0,\n delta_freeenergies: Optional[float] = None,\n molecularity: Optional[float] = None,\n volume: Optional[float] = None\n) -> float", "funcdef": "def"}, {"fullname": "overreact.api.get_kappa", "modulename": "overreact.api", "qualname": "get_kappa", "type": "function", "doc": ">>> get_k(model.scheme, model.compounds, bias=1.0 * constants.kcal,\n... temperature=300.0, scale="cm3 particle-1 s-1")\narray([5.14e-13])\n>>> get_k(model.scheme, model.compounds,\n... bias=np.array([0.0, 0.0, -1.4, 0.0, 0.0]) * constants.kcal,\n... temperature=300.0, scale="cm3 particle-1 s-1")\narray([1.1e-12])\nParameters
\n\n\n
\n\noverreact.io.parse_model.overreact.io.parse_model.Returns
\n\n\n
\n\nRaises
\n\n\n
\n\nmethod is not supported.Examples
\n\n\n\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> kappa = get_kappa(model.scheme, model.compounds)\n>>> kappa\narray([1.110])\n>>> get_kappa(model.scheme, model.compounds, method="none")\narray([1.0])\n>>> get_kappa(model.scheme, model.compounds, method="none") \\\n... == get_kappa(model.scheme, model.compounds, method=None)\narray([ True])\n\n", "signature": "(\n scheme: overreact.core.Scheme,\n compounds: dict,\n method: str = 'eckart',\n qrrho: bool = True,\n temperature: float = 298.15\n)", "funcdef": "def"}, {"fullname": "overreact.api.get_freeenergies", "modulename": "overreact.api", "qualname": "get_freeenergies", "type": "function", "doc": ">>> kappa * get_k(model.scheme, model.compounds, tunneling=None)\narray([8.e+10])\nParameters
\n\n\n
\n\noverreact.io.parse_model.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> freeenergies = get_freeenergies(model.compounds, qrrho=(False, True))\n>>> (freeenergies - freeenergies.min()) / constants.kcal\narray([0. , 2.62281461])\n>>> freeenergies = get_freeenergies(model.compounds)\n>>> (freeenergies - freeenergies.min()) / constants.kcal\narray([0. , 2.62862818])\n\n\n>>> sol_freeenergies = get_freeenergies(model.compounds, environment="solvent")\n>>> (sol_freeenergies - freeenergies) / constants.kcal\narray([1.89432845, 1.89432845])\n\n", "signature": "(\n compounds: dict,\n bias: float = 0.0,\n environment: Optional[str] = None,\n method: str = 'standard',\n qrrho: Union[bool, tuple[bool, bool]] = True,\n temperature: float = 298.15,\n pressure: float = 101325.0\n)", "funcdef": "def"}, {"fullname": "overreact.api.get_entropies", "modulename": "overreact.api", "qualname": "get_entropies", "type": "function", "doc": ">>> get_freeenergies(model.compounds, bias=1.0) - freeenergies\narray([1., 1.])\n>>> get_freeenergies(model.compounds, bias=-1.0) - freeenergies\narray([-1., -1.])\n>>> get_freeenergies(model.compounds, bias=[1.0, -1.0]) - freeenergies\narray([ 1., -1.])\nParameters
\n\n\n
\n\noverreact.io.parse_model.A(g) or A is gas,\nA(w) or A(...) is solvated). In case this is given, all compounds\nwill have the same behavior.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> entropies = get_entropies(model.compounds)\n>>> (entropies - entropies.min()) / constants.calorie\narray([1.4, 0. ])\n\n", "signature": "(\n compounds: dict,\n environment: Optional[str] = None,\n method: str = 'standard',\n qrrho: bool = True,\n temperature: float = 298.15,\n pressure: float = 101325.0\n)", "funcdef": "def"}, {"fullname": "overreact.api.get_enthalpies", "modulename": "overreact.api", "qualname": "get_enthalpies", "type": "function", "doc": ">>> sol_entropies = get_entropies(model.compounds, environment="solvent")\n>>> (sol_entropies - entropies) / constants.calorie\narray([-6.35360874, -6.35360874])\nParameters
\n\n\n
\n\noverreact.io.parse_model.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> enthalpies = get_enthalpies(model.compounds)\n>>> (enthalpies - enthalpies.min()) / constants.kcal\narray([0. , 2.20053981])\noverreact.tunnel.eckart). We can use this to calculate, for instance,\nthe thermal contributions to the enthalpy:\n", "signature": "(compounds: dict, qrrho: bool = True, temperature: float = 298.15)", "funcdef": "def"}, {"fullname": "overreact.api.get_internal_energies", "modulename": "overreact.api", "qualname": "get_internal_energies", "type": "function", "doc": ">>> zero_enthalpies = get_enthalpies(model.compounds, temperature=0)\n>>> (enthalpies - zero_enthalpies) / constants.kcal\narray([2.78, 2.50])\nParameters
\n\n\n
\n\noverreact.io.parse_model.Returns
\n\n\n
\n\nExamples
\n\n\n", "signature": "(compounds: dict, qrrho: bool = True, temperature: float = 298.15)", "funcdef": "def"}, {"fullname": "overreact.coords", "modulename": "overreact.coords", "type": "module", "doc": ">>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> internal_energies = get_internal_energies(model.compounds)\n>>> (internal_energies - internal_energies.min()) / constants.kcal\narray([0. , 2.20053981])\nParameters
\n\n\n
\n\nnumpy.isclose).numpy.isclose).Returns
\n\n\n
\n\nExamples
\n\n\n", "signature": "(atommasses, atomcoords, proper_axes=None, rtol=0.0, atol=0.01)", "funcdef": "def"}, {"fullname": "overreact.coords.symmetry_number", "modulename": "overreact.coords", "qualname": "symmetry_number", "type": "function", "doc": ">>> find_point_group([1], [[0, 0, 1]])\n'K'\n>>> find_point_group([1, 1], [[0, 0, 1], [0, 0, 0]])\n'D\u221eh'\n>>> find_point_group([1.008, 35.45], [[0, 0, 1], [0, 0, 0]])\n'C\u221ev'\n>>> find_point_group([16, 12, 16], [[1, 0, 1], [1, 0, 0], [1, 0, -1]])\n'D\u221eh'\n>>> find_point_group([16, 12, 32], [[1, 1, 1], [1, 1, 0], [1, 1, -1]])\n'C\u221ev'\n>>> find_point_group([12, 12, 12, 12], [[1, 0, 0],\n... [0, 1, 0],\n... [0, 0, 0],\n... [1, 1, 0]])\n'D4h'\nParameters
\n\n\n
\n\nReturns
\n\n\n
\n\nRaises
\n\n\n
\n\nExamples
\n\n\n", "signature": "(point_group)", "funcdef": "def"}, {"fullname": "overreact.core", "modulename": "overreact.core", "type": "module", "doc": ">>> symmetry_number("C4")\n4\n>>> symmetry_number("C4") == symmetry_number("C4h")\nTrue\n>>> symmetry_number("C6")\n6\n>>> symmetry_number("C6") == symmetry_number("C6v")\nTrue\n>>> symmetry_number("C6") == symmetry_number("C6h")\nTrue\n>>> symmetry_number("D2") == symmetry_number("Vh")\nTrue\n>>> symmetry_number("D4")\n8\n>>> symmetry_number("D6")\n12\n>>> symmetry_number("D6") == symmetry_number("D6d")\nTrue\n>>> symmetry_number("S6")\n3\n>>> symmetry_number("T")\n12\noverreact.io.parse_model.overreact.io.parse_model other details.Parameters
\n\n\n
\n\nReturns
\n\n\n
\n\nNotes
\n\n
\n\n equation ::= equation_side arrow equation_side\nequation_side ::= coefficient compound ['+' coefficient compound]*\n coefficient ::= [integers] (defaults to 1)\n compound ::= mix of printable characters\n arrow ::= '->' | '<=>' | '<-'\n//) are ignored. Repeated\nreactions are ignored. Furthermore, reactions can be chained one after\nanother and, if a single compound (with either a \u2021 or a # at the end)\nappears alone on one side of a reaction, it's considered a transition\nstate. Transition states have zero lifetime during the simulation.Examples
\n\n\n\n>>> scheme = parse_reactions("A -> B // a direct reaction")\n\n\n>>> scheme.compounds\n('A', 'B')\n>>> scheme.reactions\n('A -> B',)\n>>> scheme.is_half_equilibrium\n(False,)\n>>> scheme.A\n((-1.,), (1.,))\n>>> scheme.B\n((-1.,), (1.,))\n\n\n>>> parse_reactions("B <- A // reverse reaction of the above")\nScheme(compounds=('A', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.,), (1.,)),\n B=((-1.,), (1.,)))\n\n\n>>> parse_reactions("A <=> B // an equilibrium")\nScheme(compounds=('A', 'B'),\n reactions=('A -> B', 'B -> A'),\n is_half_equilibrium=(True, True),\n A=((-1., 1.),\n (1., -1.)),\n B=((-1., 0.),\n (1., 0.)))\n\n\n>>> parse_reactions('''\n... A <=> B -> A\n... A -> B <=> A\n... A -> B <- A\n... B <- A -> B\n... ''')\nScheme(compounds=('A', 'B'),\n reactions=('A -> B', 'B -> A'),\n is_half_equilibrium=(True, True),\n A=((-1., 1.),\n (1., -1.)),\n B=((-1., 0.),\n (1., 0.)))\n\u2021 or #). They are shown among compounds, but the matrix $A$ ensures\nthey'll never have a non-zero rate of formation/consumption. On the other\nhand, they are needed in the $B$ matrix:\n\n>>> parse_reactions("A -> A\u2021 -> B")\nScheme(compounds=('A', 'A\u2021', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.,), (0.,), (1.,)),\n B=((-1.,), (1.,), (0.,)))\n\n\n>>> parse_reactions("A -> A\u2021 -> B <- A\u2021 <- A")\nScheme(compounds=('A', 'A\u2021', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.,), (0.,), (1.,)),\n B=((-1.,), (1.,), (0.,)))\n\n\n>>> parse_reactions("S -> E\u2021 -> S")\nScheme(compounds=('S', 'E\u2021'),\n reactions=('S -> S',),\n is_half_equilibrium=(False,),\n A=((0.,), (0.,)),\n B=((-1.,), (1.,)))\n\n\n>>> parse_reactions('''\n... B -> B\u2021 -> C // chained reactions and transition states\n... B\u2021 -> D // this is a bifurcation\n... B -> B'\u2021 -> E // this is a classical competitive reaction\n... A -> B\u2021\n... ''')\nScheme(compounds=('B', 'B\u2021', 'C', 'D', "B'\u2021", 'E', 'A'),\n reactions=('B -> C', 'B -> D', 'B -> E', 'A -> C', 'A -> D'),\n is_half_equilibrium=(False, False, False, False, False),\n A=((-1., -1., -1., 0., 0.),\n (0., 0., 0., 0., 0.),\n (1., 0., 0., 1., 0.),\n (0., 1., 0., 0., 1.),\n (0., 0., 0., 0., 0.),\n (0., 0., 1., 0., 0.),\n (0., 0., 0., -1., -1.)),\n B=((-1., -1., -1., 0., 0.),\n (1., 1., 0., 1., 1.),\n (0., 0., 0., 0., 0.),\n (0., 0., 0., 0., 0.),\n (0., 0., 1., 0., 0.),\n (0., 0., 0., 0., 0.),\n (0., 0., 0., -1., -1.)))\n\n\n>>> parse_reactions('''\n... A -> A\u2021 -> B\n... A -> B\n... ''')\nScheme(compounds=('A', 'A\u2021', 'B'),\n reactions=('A -> B', 'A -> B'),\n is_half_equilibrium=(False, False),\n A=((-1., -1.),\n (0., 0.),\n (1., 1.)),\n B=((-1., -1.),\n (1., 0.),\n (0., 1.)))\n\n\n>>> parse_reactions("A -> A\u2021 -> A'\u2021 -> B")\nScheme(compounds=('A', 'A\u2021', "A'\u2021", 'B'),\n reactions=("A -> A'\u2021",),\n is_half_equilibrium=(False,),\n A=((-1.,), (0.,), (1.,), (0.,)),\n B=((-1.,), (1.,), (0.,), (0.,)))\n.k file) contains all the information needed\nto create a model input file.\nA model input file (also known as a .jk file) is a JSON encoded file with all the\ninformation needed to study microkinetic simulations from first principles.Parameters
\n\n\n
\n\n.jk or .k, it is guessed..k file will take precedence over any .jk file for reading. A\n.jk file is thus either generated or overwritten. This is sometimes\nneeded to force an update with new data.Returns
\n\n\n
\n\nRaises
\n\n\n
\n\nExamples
\n\n\n", "signature": "(path: str, force_compile: bool = False)", "funcdef": "def"}, {"fullname": "overreact.rates", "modulename": "overreact.rates", "type": "module", "doc": ">>> model = parse_model("data/ethane/B97-3c/model.jk")\n>>> model.scheme\nScheme(compounds=('S', 'E\u2021'),\n reactions=('S -> S',),\n is_half_equilibrium=(False,),\n A=((0.0,), (0.0,)),\n B=((-1.0,), (1.0,)))\n>>> model.compounds["S"]\n{'logfile': 'data/ethane/B97-3c/staggered.out',\n 'energy': -209483812.77142256,\n 'mult': 1,\n 'atomnos': (6, 6, 1, 1, 1, 1, 1, 1),\n 'atommasses': (12.011, 12.011, 1.008, 1.008, 1.008, 1.008, 1.008, 1.008),\n 'atomcoords': ((-7.633588, 2.520693, -4.8e-05),\n ...,\n (-5.832852, 3.674431, 0.363239)),\n 'vibfreqs': (307.57, 825.42, ..., 3071.11, 3071.45),\n 'vibdisps': (((-1.7e-05, 3.4e-05, 5.4e-05),\n ...,\n (-0.011061, -0.030431, -0.027036)))}\n>>> model_from_source = parse_model("data/ethane/B97-3c/model.k",\n... force_compile=True)\n>>> model_from_source == model\nTrue\n>>> model_from_source = parse_model("data/ethane/B97-3c/model")\n>>> model_from_source == model\nTrue\nParameters
\n\n\n
\n\ndelta_moles for\nequilibrium_constant, which effectively calculates a solution\nequilibrium constant between reactants and the transition state for\ngas phase data. You should set this to None if your free energies\nwere already adjusted for solution Gibbs free energies.equilibrium_constant.Returns
\n\n\n
\n\nNotes
\n\nequilibrium_constant internally to calculate the\nequilibrium constant between reactants and the transition state.Examples
\n\n\n\n>>> eyring(17.26 * constants.kcal)\narray([1.38])\n>>> eyring(18.86 * constants.kcal)\narray([0.093])\n\n\n>>> dG = np.random.uniform(1.0, 100.0) * constants.kcal\n>>> eyring(dG - 1.4 * constants.kcal) / eyring(dG)\narray([10.])\n\n", "signature": "(\n delta_freeenergy: Union[float, numpy.ndarray],\n molecularity: Optional[int] = None,\n temperature: Union[float, numpy.ndarray] = 298.15,\n pressure: float = 101325.0,\n volume: Optional[float] = None\n)", "funcdef": "def"}, {"fullname": "overreact.simulate", "modulename": "overreact.simulate", "type": "module", "doc": ">>> eyring(dG - 0.4 * constants.kcal) / eyring(dG)\narray([2.0])\nsolve_ivp\nunder the hood.Parameters
\n\n\n
\n\nscipy.integrate.solve_ivp for details.\nKinetics problems are very often stiff and, as such, \"RK45\" is\nnormally unsuited. \"Radau\", \"BDF\" or \"LSODA\" are good choices.scipy.integrate.solve_ivp for details.t_span is not given, an interval will be estimated, but it can't\nbe larger than this parameter.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import numpy as np\n>>> import overreact as rx\n\n\n>>> scheme = rx.parse_reactions("A <=> B")\n>>> y, r = get_y(get_dydt(scheme, np.array([1, 1])), y0=[1, 0])\ny object stores information about the simulation time, which can be\nused to produce a suitable vector of timepoints for, e.g., plotting:\n\n>>> y.t_min, y.t_max # doctest: +SKIP\n(0.0, 3.0)\n>>> t = np.linspace(y.t_min, y.t_max)\n>>> t # doctest: +SKIP\narray([0. , 0.06122449, ..., 2.93877551, 3. ])\ny and r can be used to check concentrations and rates in any\npoint in time. In particular, both are vectorized:\n", "signature": "(\n dydt,\n y0,\n t_span=None,\n method='Radau',\n rtol=1e-05,\n atol=1e-11,\n max_time=86400\n)", "funcdef": "def"}, {"fullname": "overreact.simulate.get_dydt", "modulename": "overreact.simulate", "qualname": "get_dydt", "type": "function", "doc": ">>> y(t) # doctest: +SKIP\narray([[1. , 0.94237559, ..., 0.5012394, 0.5 ],\n [0. , 0.05762441, ..., 0.4987606, 0.5 ]])\n>>> r(t) # doctest: +SKIP\narray([[-1.00000000e+00, ..., -1.39544265e-10],\n [ 1.00000000e+00, ..., 1.39544265e-10]])\nParameters
\n\n\n
\n\noverreact.io.parse_model.dydt.Returns
\n\n\n
\n\nk of the returned function. If JAX is\navailable, the attribute jac will hold the Jacobian function of\ndydt.Notes
\n\nscipy.integrate.solve_ivp or the older scipy.integrate.ode (see\nexamples below). This is actually what the function get_y from the\ncurrent module does.Examples
\n\n\n\n>>> import numpy as np\n>>> import overreact as rx\n\n\n>>> scheme = rx.parse_reactions("A <=> B")\n>>> dydt = get_dydt(scheme, np.array([1, 1]))\n>>> dydt(0.0, np.array([1., 1.])) # doctest: +SKIP\narray([0., 0.])\ndydt\ncomplain if given lists instead of numpy arrays. So stick to the safer,\nfaster side as above.k\nattribute of dydt:\n\n>>> dydt.k # doctest: +SKIP\narray([1., 1.])\ndydt.jac:\n", "signature": "(scheme, k, ef=5)", "funcdef": "def"}, {"fullname": "overreact.simulate.get_fixed_scheme", "modulename": "overreact.simulate", "qualname": "get_fixed_scheme", "type": "function", "doc": ">>> dydt.jac(0.0, np.array([1., 1.])) # doctest: +SKIP\nDeviceArray([[-1., 1.],\n [ 1., -1.]], dtype=float64)\nParameters
\n\n\n
\n\noverreact.io.parse_model.dydt.dydt.Returns
\n\n\n
\n\nNotes
\n\nExamples
\n\n\n\n>>> import numpy as np\n>>> import overreact as rx\n\n\n>>> pH = 7\n>>> scheme = rx.parse_reactions("AH <=> A- + H+")\n>>> k = np.array([1, 1])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH})\n>>> scheme\nScheme(compounds=('AH', 'A-'),\n reactions=('AH -> A-',\n 'A- -> AH'),\n is_half_equilibrium=(True, True),\n A=((-1.0, 1.0),\n (1.0, -1.0)),\n B=((-1.0, 0.0),\n (1.0, 0.0)))\n>>> k\narray([1.e+00, 1.e-07])\n\n\n>>> scheme = rx.parse_reactions("A + 2H2O -> B")\n>>> k = np.array([1.0])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H2O": 55.6})\n>>> scheme\nScheme(compounds=('A', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.0,),\n (1.0,)),\n B=((-1.0,),\n (1.0,)))\n>>> k\narray([3091.36])\n\n\n>>> pH = 12\n>>> scheme = rx.parse_reactions("B <- AH <=> A- + H+")\n>>> k = np.array([10.0, 1, 1])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH})\n>>> scheme\nScheme(compounds=('AH', 'B', 'A-'),\n reactions=('AH -> B',\n 'AH -> A-',\n 'A- -> AH'),\n is_half_equilibrium=(False, True, True),\n A=((-1.0, -1.0, 1.0),\n (1.0, 0.0, 0.0),\n (0.0, 1.0, -1.0)),\n B=((-1.0, -1.0, 0.0),\n (1.0, 0.0, 0.0),\n (0.0, 1.0, 0.0)))\n>>> k\narray([1.e+01, 1.e+00, 1.e-12])\n\n\n>>> pH = 2\n>>> scheme = rx.parse_reactions(["AH <=> A- + H+", "B- + H+ <=> BH"])\n>>> k = np.array([1, 1, 2, 2])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH})\n>>> scheme\nScheme(compounds=('AH', 'A-', 'B-', 'BH'),\n reactions=('AH -> A-',\n 'A- -> AH',\n 'B- -> BH',\n 'BH -> B-'),\n is_half_equilibrium=(True, True, True, True),\n A=((-1.0, 1.0, 0.0, 0.0),\n (1.0, -1.0, 0.0, 0.0),\n (0.0, 0.0, -1.0, 1.0),\n (0.0, 0.0, 1.0, -1.0)),\n B=((-1.0, 0.0, 0.0, 0.0),\n (1.0, 0.0, 0.0, 0.0),\n (0.0, 0.0, -1.0, 0.0),\n (0.0, 0.0, 1.0, 0.0)))\n>>> k\narray([1. , 0.01, 0.02, 2. ])\n\n\n>>> pH = 6\n>>> scheme = rx.parse_reactions("A + H2O -> B <=> B- + H+")\n>>> k = np.array([1.0, 100.0, 2.0])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH, "H2O": 55.6})\n>>> scheme\nScheme(compounds=('A', 'B', 'B-'),\n reactions=('A -> B',\n 'B -> B-',\n 'B- -> B'),\n is_half_equilibrium=(False, True, True),\n A=((-1.0, 0.0, 0.0),\n (1.0, -1.0, 1.0),\n (0.0, 1.0, -1.0)),\n B=((-1.0, 0.0, 0.0),\n (1.0, -1.0, 0.0),\n (0.0, 1.0, 0.0)))\n>>> k\narray([5.56e+01, 1.00e+02, 2.00e-06])\nfixed_y0 is empty, which is very important\nfor overall code consistency:\n", "signature": "(scheme, k, fixed_y0)", "funcdef": "def"}, {"fullname": "overreact.thermo", "modulename": "overreact.thermo", "type": "module", "doc": ">>> scheme = rx.parse_reactions(["AH <=> A- + H+", "B- + H+ <=> BH"])\n>>> k = np.array([1, 1, 2, 2])\n>>> new_scheme, new_k = rx.get_fixed_scheme(scheme, k, {})\n>>> new_scheme == scheme\nTrue\n>>> np.allclose(new_k, k)\nTrue\ndelta_moles is given, the above will be multiplied by a term\n\\( \\left( \\frac{R T}{p} \\right)^{-\\Delta n} \\), which effectively\ntransforms a \\( K_p \\) equilibrium constant into a \\( K_c \\)\n(see below).Parameters
\n\n\n
\n\nNone if your free energies were already adjusted\nfor solution Gibbs free energies.delta_moles.Returns
\n\n\n
\n\nNotes
\n\ndelta_moles to the difference in moles between products and reactants.\nAlternatively, convert your energies to solution Gibbs free energies and\nset delta_moles to None.Examples
\n\n\n\n>>> Kc = 0.7**2 / (0.2**2 * 0.5)\n>>> Kc\n24.5\nequilibrium_constant to reach the same result using the\nGibbs reaction free energy (which can easily be obtained from\n\\( K_c \\)):\n\n>>> temperature = 298.15\n>>> dG = -constants.R * temperature * np.log(Kc)\n>>> equilibrium_constant(dG)\narray([24.5])\ndelta_moles value (in this case, \\( 2 - 2 - 1 = -1 \\)),\nwe can calculate the corresponding K_p:\n\n>>> equilibrium_constant(dG, delta_moles=-1)\narray([1.002])\nchange_reference_state):\n\n>>> dG += temperature * rx.change_reference_state()\n>>> equilibrium_constant(dG)\narray([1.002])\ndelta_moles:\n\n>>> equilibrium_constant(dG, delta_moles=1)\narray([24.5])\n\n\n>>> dG1 = -37.4e3\n>>> np.log10(equilibrium_constant(dG1))\narray([6.55])\n>>> dG2 = -60.67e3\n>>> np.log10(equilibrium_constant(dG2))\narray([10.62])\ndelta_moles are -4 and -2,\nrespectively, and we could obtain the \\( \\log_{10}(K_p) \\) the following\nway:\n\n>>> np.log10(equilibrium_constant(dG1, delta_moles=-4))\narray([0.998])\n>>> np.log10(equilibrium_constant(dG2, delta_moles=-2))\narray([7.85])\nParameters
\n\n\n
\n\nReturns
\n\n\n
\n\nNotes
\n\nold_reference and new_reference). The easiest way to accomplish this is\nby using sign=-1 or multiplying the result by -1.Examples
\n\n\n\n>>> -rx.change_reference_state() / constants.calorie\n-6.4\n>>> 298.15 * rx.change_reference_state() / constants.kcal\n1.89\n>>> 273.15 * rx.change_reference_state(temperature=273.15) / constants.kcal\n1.69\n\n\n>>> -298.15 * rx.change_reference_state(14, 1) / constants.kcal\n-1.56\n\n", "signature": "(\n new_reference: float = 1000.0,\n old_reference: Optional[float] = None,\n sign: int = 1,\n temperature: Union[float, numpy.ndarray] = 298.15,\n pressure: float = 101325.0,\n volume: Optional[float] = None\n)", "funcdef": "def"}, {"fullname": "overreact.tunnel", "modulename": "overreact.tunnel", "type": "module", "doc": ">>> rx.change_reference_state(sign=-1) == -rx.change_reference_state()\nTrue\nParameters
\n\n\n
\n\ndelta_backward == delta_forward is assumed).Returns
\n\n\n
\n\nRaises
\n\n\n
\n\nvibfreq is zero.Examples
\n\n\n\n>>> eckart(1218, 13672.624, 24527.729644, temperature=300)\narray(3.9)\n>>> eckart(1218, 13672.624, 24527.729644, temperature=[200, 298.15])\narray([17.1, 4.0])\n>>> eckart([1218, 200], 13672.624, 24527.729644, temperature=400)\narray([2.3, 1.0])\n\n", "signature": "(\n vibfreq: float,\n delta_forward: float,\n delta_backward: Optional[float] = None,\n temperature: Union[float, numpy.ndarray] = 298.15\n) -> Union[float, numpy.ndarray]", "funcdef": "def"}, {"fullname": "overreact.tunnel.wigner", "modulename": "overreact.tunnel", "qualname": "wigner", "type": "function", "doc": ">>> eckart(414.45, 394.54)\narray(1.16)\n>>> eckart(414.45, 789.08)\narray(1.3)\n>>> eckart(3315.6, 3156.31)\narray(3.3)\nParameters
\n\n\n
\n\nReturns
\n\n\n
\n\nRaises
\n\n\n
\n\nvibfreq is zero.Examples
\n\n\n", "signature": "(\n vibfreq: float,\n temperature: Union[float, numpy.ndarray] = 298.15\n) -> float", "funcdef": "def"}];
+ /** pdoc search index */const docs = [{"fullname": "overreact", "modulename": "overreact", "type": "module", "doc": ">>> wigner(1821.0777)\n4.218\n>>> wigner(262.38)\n1.06680\n>>> wigner(113.87)\n1.01258\n>>> wigner(169.14)\n1.02776\n
\n\n\n\n \n\n
\n\n\n\n\n\nIn [1]: from overreact import api\n\nIn [2]: api.get_k("S -> E\u2021 -> S",\n ...: {"S": "data/ethane/B97-3c/staggered.out",\n ...: "E\u2021": "data/ethane/B97-3c/eclipsed.out"})\nOut[2]: array([8.16880917e+10])\n\n \n \ud83e\udd14 What is microkinetic modeling?\n
\n \n \n \ud83e\uddd0 What do you mean by first-principles calculations?\n
\n cclib (see the\nlist of its supported programs).Installation
\n\npip:\n\n$ pip install "overreact[cli,fast]"\n\n
\n\nCiting overreact
\n\n\n
\n\n\n\n@misc{overreact2021,\n howpublished = {\\url{https://github.com/geem-lab/overreact}}\n year = {2021},\n author = {Schneider, F. S. S. and Caramori, G. F.},\n title = {\n \\textbf{geem-lab/overreact}: a tool for creating and analyzing\n microkinetic models built from computational chemistry data, v1.0.2\n },\n doi = {10.5281/zenodo.5730603},\n url = {https://zenodo.org/record/5730603},\n publisher = {Zenodo},\n copyright = {Open Access}\n}\n\n
\n\nLicense
\n\nFunding
\n\nParameters
\n\n\n
\n\noverreact.io.parse_model.overreact.io.parse_model.delta_moles for\noverreact.thermo.equilibrium_constant, which effectively calculates a solution\nequilibrium constant between reactants and the transition state for\ngas phase data. You should set this to None if your free energies\nwere already adjusted for solution Gibbs free energies.Returns
\n\n\n
\n\nNotes
\n\nExamples
\n\n\n\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> get_k(model.scheme, model.compounds)\narray([8.16e+10])\n>>> get_k(model.scheme, model.compounds, qrrho=(False, True))\narray([8.24968117e+10])\n>>> get_k(model.scheme, model.compounds, qrrho=False)\narray([8.26909266e+10])\n>>> get_k(model.scheme, model.compounds, tunneling="wigner")\narray([7.99e+10])\n>>> get_k(model.scheme, model.compounds, tunneling=None)\narray([7.35e+10])\n\n\n>>> model = rx.parse_model("data/tanaka1996/UMP2/cc-pVTZ/model.jk")\n>>> get_k(model.scheme, model.compounds, temperature=300,\n... scale="cm3 particle-1 s-1")\narray([9.60e-14])\n\n\n>>> get_k(model.scheme, model.compounds) \\\n... == get_k(model.scheme, model.compounds, scale="l mol-1 s-1")\narray([ True])\n\n\n>>> get_k(model.scheme, model.compounds, tunneling="none") \\\n... == get_k(model.scheme, model.compounds, tunneling=None)\narray([ True])\n\n", "signature": "(\n scheme: overreact.core.Scheme,\n compounds: Optional[dict] = None,\n bias: float = 0.0,\n tunneling: str = 'eckart',\n qrrho: Union[bool, tuple[bool, bool]] = True,\n scale: str = 'l mol-1 s-1',\n temperature: float = 298.15,\n pressure: float = 101325.0,\n delta_freeenergies: Optional[float] = None,\n molecularity: Optional[float] = None,\n volume: Optional[float] = None\n) -> float", "funcdef": "def"}, {"fullname": "overreact.api.get_kappa", "modulename": "overreact.api", "qualname": "get_kappa", "type": "function", "doc": ">>> get_k(model.scheme, model.compounds, bias=1.0 * constants.kcal,\n... temperature=300.0, scale="cm3 particle-1 s-1")\narray([5.14e-13])\n>>> get_k(model.scheme, model.compounds,\n... bias=np.array([0.0, 0.0, -1.4, 0.0, 0.0]) * constants.kcal,\n... temperature=300.0, scale="cm3 particle-1 s-1")\narray([1.1e-12])\nParameters
\n\n\n
\n\noverreact.io.parse_model.overreact.io.parse_model.Returns
\n\n\n
\n\nRaises
\n\n\n
\n\nmethod is not supported.Examples
\n\n\n\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> kappa = get_kappa(model.scheme, model.compounds)\n>>> kappa\narray([1.110])\n>>> get_kappa(model.scheme, model.compounds, method="none")\narray([1.0])\n>>> get_kappa(model.scheme, model.compounds, method="none") \\\n... == get_kappa(model.scheme, model.compounds, method=None)\narray([ True])\n\n", "signature": "(\n scheme: overreact.core.Scheme,\n compounds: dict,\n method: str = 'eckart',\n qrrho: bool = True,\n temperature: float = 298.15\n)", "funcdef": "def"}, {"fullname": "overreact.api.get_freeenergies", "modulename": "overreact.api", "qualname": "get_freeenergies", "type": "function", "doc": ">>> kappa * get_k(model.scheme, model.compounds, tunneling=None)\narray([8.e+10])\nParameters
\n\n\n
\n\noverreact.io.parse_model.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> freeenergies = get_freeenergies(model.compounds, qrrho=(False, True))\n>>> (freeenergies - freeenergies.min()) / constants.kcal\narray([0. , 2.62281461])\n>>> freeenergies = get_freeenergies(model.compounds)\n>>> (freeenergies - freeenergies.min()) / constants.kcal\narray([0. , 2.62862818])\n\n\n>>> sol_freeenergies = get_freeenergies(model.compounds, environment="solvent")\n>>> (sol_freeenergies - freeenergies) / constants.kcal\narray([1.89432845, 1.89432845])\n\n", "signature": "(\n compounds: dict,\n bias: float = 0.0,\n environment: Optional[str] = None,\n method: str = 'standard',\n qrrho: Union[bool, tuple[bool, bool]] = True,\n temperature: float = 298.15,\n pressure: float = 101325.0\n)", "funcdef": "def"}, {"fullname": "overreact.api.get_entropies", "modulename": "overreact.api", "qualname": "get_entropies", "type": "function", "doc": ">>> get_freeenergies(model.compounds, bias=1.0) - freeenergies\narray([1., 1.])\n>>> get_freeenergies(model.compounds, bias=-1.0) - freeenergies\narray([-1., -1.])\n>>> get_freeenergies(model.compounds, bias=[1.0, -1.0]) - freeenergies\narray([ 1., -1.])\nParameters
\n\n\n
\n\noverreact.io.parse_model.A(g) or A is gas,\nA(w) or A(...) is solvated). In case this is given, all compounds\nwill have the same behavior.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> entropies = get_entropies(model.compounds)\n>>> (entropies - entropies.min()) / constants.calorie\narray([1.4, 0. ])\n\n", "signature": "(\n compounds: dict,\n environment: Optional[str] = None,\n method: str = 'standard',\n qrrho: bool = True,\n temperature: float = 298.15,\n pressure: float = 101325.0\n)", "funcdef": "def"}, {"fullname": "overreact.api.get_enthalpies", "modulename": "overreact.api", "qualname": "get_enthalpies", "type": "function", "doc": ">>> sol_entropies = get_entropies(model.compounds, environment="solvent")\n>>> (sol_entropies - entropies) / constants.calorie\narray([-6.35360874, -6.35360874])\nParameters
\n\n\n
\n\noverreact.io.parse_model.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> enthalpies = get_enthalpies(model.compounds)\n>>> (enthalpies - enthalpies.min()) / constants.kcal\narray([0. , 2.20053981])\noverreact.tunnel.eckart). We can use this to calculate, for instance,\nthe thermal contributions to the enthalpy:\n", "signature": "(compounds: dict, qrrho: bool = True, temperature: float = 298.15)", "funcdef": "def"}, {"fullname": "overreact.api.get_internal_energies", "modulename": "overreact.api", "qualname": "get_internal_energies", "type": "function", "doc": ">>> zero_enthalpies = get_enthalpies(model.compounds, temperature=0)\n>>> (enthalpies - zero_enthalpies) / constants.kcal\narray([2.78, 2.50])\nParameters
\n\n\n
\n\noverreact.io.parse_model.Returns
\n\n\n
\n\nExamples
\n\n\n", "signature": "(compounds: dict, qrrho: bool = True, temperature: float = 298.15)", "funcdef": "def"}, {"fullname": "overreact.coords", "modulename": "overreact.coords", "type": "module", "doc": ">>> import overreact as rx\n>>> from overreact import _constants as constants\n>>> model = rx.parse_model("data/ethane/B97-3c/model.k")\n>>> internal_energies = get_internal_energies(model.compounds)\n>>> (internal_energies - internal_energies.min()) / constants.kcal\narray([0. , 2.20053981])\nParameters
\n\n\n
\n\nnumpy.isclose).numpy.isclose).Returns
\n\n\n
\n\nExamples
\n\n\n", "signature": "(atommasses, atomcoords, proper_axes=None, rtol=0.0, atol=0.01)", "funcdef": "def"}, {"fullname": "overreact.coords.symmetry_number", "modulename": "overreact.coords", "qualname": "symmetry_number", "type": "function", "doc": ">>> find_point_group([1], [[0, 0, 1]])\n'K'\n>>> find_point_group([1, 1], [[0, 0, 1], [0, 0, 0]])\n'D\u221eh'\n>>> find_point_group([1.008, 35.45], [[0, 0, 1], [0, 0, 0]])\n'C\u221ev'\n>>> find_point_group([16, 12, 16], [[1, 0, 1], [1, 0, 0], [1, 0, -1]])\n'D\u221eh'\n>>> find_point_group([16, 12, 32], [[1, 1, 1], [1, 1, 0], [1, 1, -1]])\n'C\u221ev'\n>>> find_point_group([12, 12, 12, 12], [[1, 0, 0],\n... [0, 1, 0],\n... [0, 0, 0],\n... [1, 1, 0]])\n'D4h'\nParameters
\n\n\n
\n\nReturns
\n\n\n
\n\nRaises
\n\n\n
\n\nExamples
\n\n\n", "signature": "(point_group)", "funcdef": "def"}, {"fullname": "overreact.core", "modulename": "overreact.core", "type": "module", "doc": ">>> symmetry_number("C4")\n4\n>>> symmetry_number("C4") == symmetry_number("C4h")\nTrue\n>>> symmetry_number("C6")\n6\n>>> symmetry_number("C6") == symmetry_number("C6v")\nTrue\n>>> symmetry_number("C6") == symmetry_number("C6h")\nTrue\n>>> symmetry_number("D2") == symmetry_number("Vh")\nTrue\n>>> symmetry_number("D4")\n8\n>>> symmetry_number("D6")\n12\n>>> symmetry_number("D6") == symmetry_number("D6d")\nTrue\n>>> symmetry_number("S6")\n3\n>>> symmetry_number("T")\n12\noverreact.io.parse_model.overreact.io.parse_model other details.Parameters
\n\n\n
\n\nReturns
\n\n\n
\n\nNotes
\n\n
\n\n equation ::= equation_side arrow equation_side\nequation_side ::= coefficient compound ['+' coefficient compound]*\n coefficient ::= [integers] (defaults to 1)\n compound ::= mix of printable characters\n arrow ::= '->' | '<=>' | '<-'\n//) are ignored. Repeated\nreactions are ignored. Furthermore, reactions can be chained one after\nanother and, if a single compound (with either a \u2021 or a # at the end)\nappears alone on one side of a reaction, it's considered a transition\nstate. Transition states have zero lifetime during the simulation.Examples
\n\n\n\n>>> scheme = parse_reactions("A -> B // a direct reaction")\n\n\n>>> scheme.compounds\n('A', 'B')\n>>> scheme.reactions\n('A -> B',)\n>>> scheme.is_half_equilibrium\n(False,)\n>>> scheme.A\n((-1.,), (1.,))\n>>> scheme.B\n((-1.,), (1.,))\n\n\n>>> parse_reactions("B <- A // reverse reaction of the above")\nScheme(compounds=('A', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.,), (1.,)),\n B=((-1.,), (1.,)))\n\n\n>>> parse_reactions("A <=> B // an equilibrium")\nScheme(compounds=('A', 'B'),\n reactions=('A -> B', 'B -> A'),\n is_half_equilibrium=(True, True),\n A=((-1., 1.),\n (1., -1.)),\n B=((-1., 0.),\n (1., 0.)))\n\n\n>>> parse_reactions('''\n... A <=> B -> A\n... A -> B <=> A\n... A -> B <- A\n... B <- A -> B\n... ''')\nScheme(compounds=('A', 'B'),\n reactions=('A -> B', 'B -> A'),\n is_half_equilibrium=(True, True),\n A=((-1., 1.),\n (1., -1.)),\n B=((-1., 0.),\n (1., 0.)))\n\u2021 or #). They are shown among compounds, but the matrix $A$ ensures\nthey'll never have a non-zero rate of formation/consumption. On the other\nhand, they are needed in the $B$ matrix:\n\n>>> parse_reactions("A -> A\u2021 -> B")\nScheme(compounds=('A', 'A\u2021', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.,), (0.,), (1.,)),\n B=((-1.,), (1.,), (0.,)))\n\n\n>>> parse_reactions("A -> A\u2021 -> B <- A\u2021 <- A")\nScheme(compounds=('A', 'A\u2021', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.,), (0.,), (1.,)),\n B=((-1.,), (1.,), (0.,)))\n\n\n>>> parse_reactions("S -> E\u2021 -> S")\nScheme(compounds=('S', 'E\u2021'),\n reactions=('S -> S',),\n is_half_equilibrium=(False,),\n A=((0.,), (0.,)),\n B=((-1.,), (1.,)))\n\n\n>>> parse_reactions('''\n... B -> B\u2021 -> C // chained reactions and transition states\n... B\u2021 -> D // this is a bifurcation\n... B -> B'\u2021 -> E // this is a classical competitive reaction\n... A -> B\u2021\n... ''')\nScheme(compounds=('B', 'B\u2021', 'C', 'D', "B'\u2021", 'E', 'A'),\n reactions=('B -> C', 'B -> D', 'B -> E', 'A -> C', 'A -> D'),\n is_half_equilibrium=(False, False, False, False, False),\n A=((-1., -1., -1., 0., 0.),\n (0., 0., 0., 0., 0.),\n (1., 0., 0., 1., 0.),\n (0., 1., 0., 0., 1.),\n (0., 0., 0., 0., 0.),\n (0., 0., 1., 0., 0.),\n (0., 0., 0., -1., -1.)),\n B=((-1., -1., -1., 0., 0.),\n (1., 1., 0., 1., 1.),\n (0., 0., 0., 0., 0.),\n (0., 0., 0., 0., 0.),\n (0., 0., 1., 0., 0.),\n (0., 0., 0., 0., 0.),\n (0., 0., 0., -1., -1.)))\n\n\n>>> parse_reactions('''\n... A -> A\u2021 -> B\n... A -> B\n... ''')\nScheme(compounds=('A', 'A\u2021', 'B'),\n reactions=('A -> B', 'A -> B'),\n is_half_equilibrium=(False, False),\n A=((-1., -1.),\n (0., 0.),\n (1., 1.)),\n B=((-1., -1.),\n (1., 0.),\n (0., 1.)))\n\n\n>>> parse_reactions("A -> A\u2021 -> A'\u2021 -> B")\nScheme(compounds=('A', 'A\u2021', "A'\u2021", 'B'),\n reactions=("A -> A'\u2021",),\n is_half_equilibrium=(False,),\n A=((-1.,), (0.,), (1.,), (0.,)),\n B=((-1.,), (1.,), (0.,), (0.,)))\n.k file) contains all the information needed\nto create a model input file.\nA model input file (also known as a .jk file) is a JSON encoded file with all the\ninformation needed to study microkinetic simulations from first principles.Parameters
\n\n\n
\n\n.jk or .k, it is guessed..k file will take precedence over any .jk file for reading. A\n.jk file is thus either generated or overwritten. This is sometimes\nneeded to force an update with new data.Returns
\n\n\n
\n\nRaises
\n\n\n
\n\nExamples
\n\n\n", "signature": "(path: str, force_compile: bool = False)", "funcdef": "def"}, {"fullname": "overreact.rates", "modulename": "overreact.rates", "type": "module", "doc": ">>> model = parse_model("data/ethane/B97-3c/model.jk")\n>>> model.scheme\nScheme(compounds=('S', 'E\u2021'),\n reactions=('S -> S',),\n is_half_equilibrium=(False,),\n A=((0.0,), (0.0,)),\n B=((-1.0,), (1.0,)))\n>>> model.compounds["S"]\n{'logfile': 'data/ethane/B97-3c/staggered.out',\n 'energy': -209483812.77142256,\n 'mult': 1,\n 'atomnos': (6, 6, 1, 1, 1, 1, 1, 1),\n 'atommasses': (12.011, 12.011, 1.008, 1.008, 1.008, 1.008, 1.008, 1.008),\n 'atomcoords': ((-7.633588, 2.520693, -4.8e-05),\n ...,\n (-5.832852, 3.674431, 0.363239)),\n 'vibfreqs': (307.57, 825.42, ..., 3071.11, 3071.45),\n 'vibdisps': (((-1.7e-05, 3.4e-05, 5.4e-05),\n ...,\n (-0.011061, -0.030431, -0.027036)))}\n>>> model_from_source = parse_model("data/ethane/B97-3c/model.k",\n... force_compile=True)\n>>> model_from_source == model\nTrue\n>>> model_from_source = parse_model("data/ethane/B97-3c/model")\n>>> model_from_source == model\nTrue\nParameters
\n\n\n
\n\ndelta_moles for\nequilibrium_constant, which effectively calculates a solution\nequilibrium constant between reactants and the transition state for\ngas phase data. You should set this to None if your free energies\nwere already adjusted for solution Gibbs free energies.equilibrium_constant.Returns
\n\n\n
\n\nNotes
\n\nequilibrium_constant internally to calculate the\nequilibrium constant between reactants and the transition state.Examples
\n\n\n\n>>> eyring(17.26 * constants.kcal)\narray([1.38])\n>>> eyring(18.86 * constants.kcal)\narray([0.093])\n\n\n>>> dG = np.random.uniform(1.0, 100.0) * constants.kcal\n>>> eyring(dG - 1.4 * constants.kcal) / eyring(dG)\narray([10.])\n\n", "signature": "(\n delta_freeenergy: Union[float, numpy.ndarray],\n molecularity: Optional[int] = None,\n temperature: Union[float, numpy.ndarray] = 298.15,\n pressure: float = 101325.0,\n volume: Optional[float] = None\n)", "funcdef": "def"}, {"fullname": "overreact.simulate", "modulename": "overreact.simulate", "type": "module", "doc": ">>> eyring(dG - 0.4 * constants.kcal) / eyring(dG)\narray([2.0])\nsolve_ivp\nunder the hood.Parameters
\n\n\n
\n\nscipy.integrate.solve_ivp for details.\nKinetics problems are very often stiff and, as such, \"RK45\" is\nnormally unsuited. \"Radau\", \"BDF\" or \"LSODA\" are good choices.scipy.integrate.solve_ivp for details.t_span is not given, an interval will be estimated, but it can't\nbe larger than this parameter.Returns
\n\n\n
\n\nExamples
\n\n\n\n>>> import numpy as np\n>>> import overreact as rx\n\n\n>>> scheme = rx.parse_reactions("A <=> B")\n>>> y, r = get_y(get_dydt(scheme, np.array([1, 1])), y0=[1, 0])\ny object stores information about the simulation time, which can be\nused to produce a suitable vector of timepoints for, e.g., plotting:\n\n>>> y.t_min, y.t_max # doctest: +SKIP\n(0.0, 3.0)\n>>> t = np.linspace(y.t_min, y.t_max)\n>>> t # doctest: +SKIP\narray([0. , 0.06122449, ..., 2.93877551, 3. ])\ny and r can be used to check concentrations and rates in any\npoint in time. In particular, both are vectorized:\n", "signature": "(\n dydt,\n y0,\n t_span=None,\n method='Radau',\n rtol=1e-05,\n atol=1e-11,\n max_time=86400\n)", "funcdef": "def"}, {"fullname": "overreact.simulate.get_dydt", "modulename": "overreact.simulate", "qualname": "get_dydt", "type": "function", "doc": ">>> y(t) # doctest: +SKIP\narray([[1. , 0.94237559, ..., 0.5012394, 0.5 ],\n [0. , 0.05762441, ..., 0.4987606, 0.5 ]])\n>>> r(t) # doctest: +SKIP\narray([[-1.00000000e+00, ..., -1.39544265e-10],\n [ 1.00000000e+00, ..., 1.39544265e-10]])\nParameters
\n\n\n
\n\noverreact.io.parse_model.dydt.Returns
\n\n\n
\n\nk of the returned function. If JAX is\navailable, the attribute jac will hold the Jacobian function of\ndydt.Notes
\n\nscipy.integrate.solve_ivp or the older scipy.integrate.ode (see\nexamples below). This is actually what the function get_y from the\ncurrent module does.Examples
\n\n\n\n>>> import numpy as np\n>>> import overreact as rx\n\n\n>>> scheme = rx.parse_reactions("A <=> B")\n>>> dydt = get_dydt(scheme, np.array([1, 1]))\n>>> dydt(0.0, np.array([1., 1.])) # doctest: +SKIP\narray([0., 0.])\ndydt\ncomplain if given lists instead of numpy arrays. So stick to the safer,\nfaster side as above.k\nattribute of dydt:\n\n>>> dydt.k # doctest: +SKIP\narray([1., 1.])\ndydt.jac:\n", "signature": "(scheme, k, ef=5)", "funcdef": "def"}, {"fullname": "overreact.simulate.get_fixed_scheme", "modulename": "overreact.simulate", "qualname": "get_fixed_scheme", "type": "function", "doc": ">>> dydt.jac(0.0, np.array([1., 1.])) # doctest: +SKIP\nDeviceArray([[-1., 1.],\n [ 1., -1.]], dtype=float64)\nParameters
\n\n\n
\n\noverreact.io.parse_model.dydt.dydt.Returns
\n\n\n
\n\nNotes
\n\nExamples
\n\n\n\n>>> import numpy as np\n>>> import overreact as rx\n\n\n>>> pH = 7\n>>> scheme = rx.parse_reactions("AH <=> A- + H+")\n>>> k = np.array([1, 1])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH})\n>>> scheme\nScheme(compounds=('AH', 'A-'),\n reactions=('AH -> A-',\n 'A- -> AH'),\n is_half_equilibrium=(True, True),\n A=((-1.0, 1.0),\n (1.0, -1.0)),\n B=((-1.0, 0.0),\n (1.0, 0.0)))\n>>> k\narray([1.e+00, 1.e-07])\n\n\n>>> scheme = rx.parse_reactions("A + 2H2O -> B")\n>>> k = np.array([1.0])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H2O": 55.6})\n>>> scheme\nScheme(compounds=('A', 'B'),\n reactions=('A -> B',),\n is_half_equilibrium=(False,),\n A=((-1.0,),\n (1.0,)),\n B=((-1.0,),\n (1.0,)))\n>>> k\narray([3091.36])\n\n\n>>> pH = 12\n>>> scheme = rx.parse_reactions("B <- AH <=> A- + H+")\n>>> k = np.array([10.0, 1, 1])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH})\n>>> scheme\nScheme(compounds=('AH', 'B', 'A-'),\n reactions=('AH -> B',\n 'AH -> A-',\n 'A- -> AH'),\n is_half_equilibrium=(False, True, True),\n A=((-1.0, -1.0, 1.0),\n (1.0, 0.0, 0.0),\n (0.0, 1.0, -1.0)),\n B=((-1.0, -1.0, 0.0),\n (1.0, 0.0, 0.0),\n (0.0, 1.0, 0.0)))\n>>> k\narray([1.e+01, 1.e+00, 1.e-12])\n\n\n>>> pH = 2\n>>> scheme = rx.parse_reactions(["AH <=> A- + H+", "B- + H+ <=> BH"])\n>>> k = np.array([1, 1, 2, 2])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH})\n>>> scheme\nScheme(compounds=('AH', 'A-', 'B-', 'BH'),\n reactions=('AH -> A-',\n 'A- -> AH',\n 'B- -> BH',\n 'BH -> B-'),\n is_half_equilibrium=(True, True, True, True),\n A=((-1.0, 1.0, 0.0, 0.0),\n (1.0, -1.0, 0.0, 0.0),\n (0.0, 0.0, -1.0, 1.0),\n (0.0, 0.0, 1.0, -1.0)),\n B=((-1.0, 0.0, 0.0, 0.0),\n (1.0, 0.0, 0.0, 0.0),\n (0.0, 0.0, -1.0, 0.0),\n (0.0, 0.0, 1.0, 0.0)))\n>>> k\narray([1. , 0.01, 0.02, 2. ])\n\n\n>>> pH = 6\n>>> scheme = rx.parse_reactions("A + H2O -> B <=> B- + H+")\n>>> k = np.array([1.0, 100.0, 2.0])\n>>> scheme, k = rx.get_fixed_scheme(scheme, k, {"H+": 10**-pH, "H2O": 55.6})\n>>> scheme\nScheme(compounds=('A', 'B', 'B-'),\n reactions=('A -> B',\n 'B -> B-',\n 'B- -> B'),\n is_half_equilibrium=(False, True, True),\n A=((-1.0, 0.0, 0.0),\n (1.0, -1.0, 1.0),\n (0.0, 1.0, -1.0)),\n B=((-1.0, 0.0, 0.0),\n (1.0, -1.0, 0.0),\n (0.0, 1.0, 0.0)))\n>>> k\narray([5.56e+01, 1.00e+02, 2.00e-06])\nfixed_y0 is empty, which is very important\nfor overall code consistency:\n", "signature": "(scheme, k, fixed_y0)", "funcdef": "def"}, {"fullname": "overreact.thermo", "modulename": "overreact.thermo", "type": "module", "doc": ">>> scheme = rx.parse_reactions(["AH <=> A- + H+", "B- + H+ <=> BH"])\n>>> k = np.array([1, 1, 2, 2])\n>>> new_scheme, new_k = rx.get_fixed_scheme(scheme, k, {})\n>>> new_scheme == scheme\nTrue\n>>> np.allclose(new_k, k)\nTrue\ndelta_moles is given, the above will be multiplied by a term\n\\( \\left( \\frac{R T}{p} \\right)^{-\\Delta n} \\), which effectively\ntransforms a \\( K_p \\) equilibrium constant into a \\( K_c \\)\n(see below).Parameters
\n\n\n
\n\nNone if your free energies were already adjusted\nfor solution Gibbs free energies.delta_moles.Returns
\n\n\n
\n\nNotes
\n\ndelta_moles to the difference in moles between products and reactants.\nAlternatively, convert your energies to solution Gibbs free energies and\nset delta_moles to None.Examples
\n\n\n\n>>> Kc = 0.7**2 / (0.2**2 * 0.5)\n>>> Kc\n24.5\nequilibrium_constant to reach the same result using the\nGibbs reaction free energy (which can easily be obtained from\n\\( K_c \\)):\n\n>>> temperature = 298.15\n>>> dG = -constants.R * temperature * np.log(Kc)\n>>> equilibrium_constant(dG)\narray([24.5])\ndelta_moles value (in this case, \\( 2 - 2 - 1 = -1 \\)),\nwe can calculate the corresponding K_p:\n\n>>> equilibrium_constant(dG, delta_moles=-1)\narray([1.002])\nchange_reference_state):\n\n>>> dG += temperature * rx.change_reference_state()\n>>> equilibrium_constant(dG)\narray([1.002])\ndelta_moles:\n\n>>> equilibrium_constant(dG, delta_moles=1)\narray([24.5])\n\n\n>>> dG1 = -37.4e3\n>>> np.log10(equilibrium_constant(dG1))\narray([6.55])\n>>> dG2 = -60.67e3\n>>> np.log10(equilibrium_constant(dG2))\narray([10.62])\ndelta_moles are -4 and -2,\nrespectively, and we could obtain the \\( \\log_{10}(K_p) \\) the following\nway:\n\n>>> np.log10(equilibrium_constant(dG1, delta_moles=-4))\narray([0.998])\n>>> np.log10(equilibrium_constant(dG2, delta_moles=-2))\narray([7.85])\nParameters
\n\n\n
\n\nReturns
\n\n\n
\n\nNotes
\n\nold_reference and new_reference). The easiest way to accomplish this is\nby using sign=-1 or multiplying the result by -1.Examples
\n\n\n\n>>> -rx.change_reference_state() / constants.calorie\n-6.4\n>>> 298.15 * rx.change_reference_state() / constants.kcal\n1.89\n>>> 273.15 * rx.change_reference_state(temperature=273.15) / constants.kcal\n1.69\n\n\n>>> -298.15 * rx.change_reference_state(14, 1) / constants.kcal\n-1.56\n\n", "signature": "(\n new_reference: float = 1000.0,\n old_reference: Optional[float] = None,\n sign: int = 1,\n temperature: Union[float, numpy.ndarray] = 298.15,\n pressure: float = 101325.0,\n volume: Optional[float] = None\n)", "funcdef": "def"}, {"fullname": "overreact.tunnel", "modulename": "overreact.tunnel", "type": "module", "doc": ">>> rx.change_reference_state(sign=-1) == -rx.change_reference_state()\nTrue\nParameters
\n\n\n
\n\ndelta_backward == delta_forward is assumed).Returns
\n\n\n
\n\nRaises
\n\n\n
\n\nvibfreq is zero.Examples
\n\n\n\n>>> eckart(1218, 13672.624, 24527.729644, temperature=300)\narray(3.9)\n>>> eckart(1218, 13672.624, 24527.729644, temperature=[200, 298.15])\narray([17.1, 4.0])\n>>> eckart([1218, 200], 13672.624, 24527.729644, temperature=400)\narray([2.3, 1.0])\n\n", "signature": "(\n vibfreq: float,\n delta_forward: float,\n delta_backward: Optional[float] = None,\n temperature: Union[float, numpy.ndarray] = 298.15\n) -> Union[float, numpy.ndarray]", "funcdef": "def"}, {"fullname": "overreact.tunnel.wigner", "modulename": "overreact.tunnel", "qualname": "wigner", "type": "function", "doc": ">>> eckart(414.45, 394.54)\narray(1.16)\n>>> eckart(414.45, 789.08)\narray(1.3)\n>>> eckart(3315.6, 3156.31)\narray(3.3)\nParameters
\n\n\n
\n\nReturns
\n\n\n
\n\nRaises
\n\n\n
\n\nvibfreq is zero.Examples
\n\n\n", "signature": "(\n vibfreq: float,\n temperature: Union[float, numpy.ndarray] = 298.15\n) -> float", "funcdef": "def"}];
// mirrored in build-search-index.js (part 1)
// Also split on html tags. this is a cheap heuristic, but good enough.
>>> wigner(1821.0777)\n4.218\n>>> wigner(262.38)\n1.06680\n>>> wigner(113.87)\n1.01258\n>>> wigner(169.14)\n1.02776\n