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@njzjz-bot 2312.15492

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@njzjz-bot 2312.15492

[github-actions]

@njzjz Here is the BibTeX entry for 2312.15492 :

@Article{Zhang_arXiv_2023_p2312.15492,
    author =   {Duo Zhang and Xinzijian Liu and Xiangyu Zhang and Chengqian Zhang and
             Chun Cai and Hangrui Bi and Yiming Du and Xuejian Qin and Jiameng
             Huang and Bowen Li and Yifan Shan and Jinzhe Zeng and Yuzhi Zhang and
             Siyuan Liu and Yifan Li and Junhan Chang and Xinyan Wang and Shuo Zhou
             and Jianchuan Liu and Xiaoshan Luo and Zhenyu Wang and Wanrun Jiang
             and Jing Wu and Yudi Yang and Jiyuan Yang and Manyi Yang and Fu-Qiang
             Gong and Linshuang Zhang and Mengchao Shi and Fu-Zhi Dai and Darrin M.
             York and Shi Liu and Tong Zhu and Zhicheng Zhong and Jian Lv and Jun
             Cheng and Weile Jia and Mohan Chen and Guolin Ke and Weinan E and
             Linfeng Zhang and Han Wang},
    title =    {{DPA-2: Towards a universal large atomic model for molecular and
             material simulation}},
    journal =  {arXiv},
    year =     2023,
    pages =    {2312.15492},
    doi =      {10.48550/arXiv.2312.15492},
    annote =   {The rapid development of artificial intelligence (AI) is driving
             significant changes in the field of atomic modeling, simulation, and
             design. AI-based potential energy models have been successfully used
             to perform large-scale and long-time simulations with the accuracy of
             ab initio electronic structure methods. However, the model generation
             process still hinders applications at scale. We envision that the next
             stage would be a model-centric ecosystem, in which a large atomic
             model (LAM), pre-trained with as many atomic datasets as possible and
             can be efficiently fine-tuned and distilled to downstream tasks, would
             serve the new infrastructure of the field of molecular modeling. We
             propose DPA-2, a novel architecture for a LAM, and develop a
             comprehensive pipeline for model fine-tuning, distillation, and
             application, associated with automatic workflows. We show that DPA-2
             can accurately represent a diverse range of chemical systems and
             materials, enabling high-quality simulations and predictions with
             significantly reduced efforts compared to traditional methods. Our
             approach paves the way for a universal large atomic model that can be
             widely applied in molecular and material simulation research, opening
             new opportunities for scientific discoveries and industrial
             applications.},
}

[njzjz]

@njzjz-bot 10.1038/s41524-024-01278-7

[github-actions]

@njzjz Here is the BibTeX entry for 10.1038/s41524-024-01278-7:

@Article{Zhang_NpjComputMater_2024_v10_p94,
    author =   {Duo Zhang and Hangrui Bi and Fu-Zhi Dai and Wanrun Jiang and Xinzijian
             Liu and Linfeng Zhang and Han Wang},
    title =    {{Pretraining of attention-based deep learning potential model for
             molecular simulation}},
    journal =  {Npj Comput. Mater},
    year =     2024,
    volume =   10,
    issue =    1,
    pages =    94,
    doi =      {10.1038/s41524-024-01278-7},
    annote =   {<jats:title>Abstract</jats:title><jats:p>Machine learning-assisted
             modeling of the inter-atomic potential energy surface (PES) is
             revolutionizing the field of molecular simulation. With the
             accumulation of high-quality electronic structure data, a model that
             can be pretrained on all available data and finetuned on downstream
             tasks with a small additional effort would bring the field to a new
             stage. Here we propose DPA-1, a Deep Potential model with a gated
             attention mechanism, which is highly effective for representing the
             conformation and chemical spaces of atomic systems and learning the
             PES. We tested DPA-1 on a number of systems and observed superior
             performance compared with existing benchmarks. When pretrained on
             large-scale datasets containing 56 elements, DPA-1 can be successfully
             applied to various downstream tasks with a great improvement of sample
             efficiency. Surprisingly, for different elements, the learned type
             embedding parameters form a <jats:italic>s</jats:italic><jats:italic>p
             </jats:italic><jats:italic>i</jats:italic><jats:italic>r</jats:italic>
             <jats:italic>a</jats:italic><jats:italic>l</jats:italic> in the latent
             space and have a natural correspondence with their positions on the
             periodic table, showing interesting interpretability of the pretrained
             DPA-1 model.</jats:p>},
}

[njzjz]

@njzjz-bot 10.1021/acs.jctc.1c00201

[njzjz]

@njzjz-bot 10.1021/acs.jctc.1c00201

[njzjz]

@njzjz-bot 10.1021/acs.jctc.1c00201

[njzjz]

@njzjz-bot 10.1021/acs.jctc.1c00201

[github-actions]

@njzjz Here is the BibTeX entry for 10.1021/acs.jctc.1c00201:

@Article{Zeng_JChemTheoryComput_2021_v17_p6993,
    author =   {Jinzhe Zeng and Timothy J. Giese and {\c{S}}{\"o}len Ekesan and Darrin
             M. York},
    title =    {{Development of Range-Corrected Deep Learning Potentials for Fast,
             Accurate Quantum Mechanical/Molecular Mechanical Simulations of
             Chemical Reactions in Solution}},
    journal =  {J. Chem. Theory Comput.},
    year =     2021,
    volume =   17,
    number =   11,
    pages =    {6993--7009},
    doi =      {10.1021/acs.jctc.1c00201},
    abstract = {We develop a new deep potential-range correction (DPRc) machine
             learning potential for combined quantum mechanical/molecular
             mechanical (QM/MM) simulations of chemical reactions in the condensed
             phase. The new range correction enables short-ranged QM/MM
             interactions to be tuned for higher accuracy, and the correction
             smoothly vanishes within a specified cutoff. We further develop an
             active learning procedure for robust neural network training. We test
             the DPRc model and training procedure against a series of six
             nonenzymatic phosphoryl transfer reactions in solution that are
             important in mechanistic studies of RNA-cleaving enzymes.
             Specifically, we apply DPRc corrections to a base QM model and test
             its ability to reproduce free-energy profiles generated from a target
             QM model. We perform these comparisons using the MNDO/d and DFTB2
             semiempirical models because they differ in the way they treat orbital
             orthogonalization and electrostatics and produce free-energy profiles
             which differ significantly from each other, thereby providing us a
             rigorous stress test for the DPRc model and training procedure. The
             comparisons show that accurate reproduction of the free-energy
             profiles requires correction of the QM/MM interactions out to 6
             {\r{A}}. We further find that the model's initial training benefits
             from generating data from temperature replica exchange simulations and
             including high-temperature configurations into the fitting procedure,
             so the resulting models are trained to properly avoid high-energy
             regions. A single DPRc model was trained to reproduce four different
             reactions and yielded good agreement with the free-energy profiles
             made from the target QM/MM simulations. The DPRc model was further
             demonstrated to be transferable to 2D free-energy surfaces and 1D
             free-energy profiles that were not explicitly considered in the
             training. Examination of the computational performance of the DPRc
             model showed that it was fairly slow when run on CPUs but was sped up
             almost 100-fold when using NVIDIA V100 GPUs, resulting in almost
             negligible overhead. The new DPRc model and training procedure provide
             a potentially powerful new tool for the creation of next-generation
             QM/MM potentials for a wide spectrum of free-energy applications
             ranging from drug discovery to enzyme design.},
}

[njzjz]

@njzjz-bot 10.1063/5.0211276

[github-actions]

@njzjz Here is the BibTeX entry for 10.1063/5.0211276:

@Article{Tao_JChemPhys_2024_v160_p224104,
    author =   {Yujun Tao and Timothy J. Giese and {\c{S}}{\"o}len Ekesan and Jinzhe
             Zeng and B{\'a}lint Aradi and Ben Hourahine and Hasan Metin Aktulga
             and Andreas W. G{\"o}tz and Kenneth M. {Merz Jr} and Darrin M. York},
    title =    {{Amber free energy tools: Interoperable software for free energy
             simulations using generalized quantum mechanical/molecular mechanical
             and machine learning potentials}},
    journal =  {J. Chem. Phys.},
    year =     2024,
    volume =   160,
    number =   22,
    pages =    224104,
    doi =      {10.1063/5.0211276},
    abstract = {We report the development and testing of new integrated
             cyberinfrastructure for performing free energy simulations with
             generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and
             machine learning potentials (MLPs) in Amber. The Sander molecular
             dynamics program has been extended to leverage fast, density-
             functional tight-binding models implemented in the DFTB+ and xTB
             packages, and an interface to the DeePMD-kit software enables the use
             of MLPs. The software is integrated through application program
             interfaces that circumvent the need to perform "system calls" and
             enable the incorporation of long-range Ewald electrostatics into the
             external software's self-consistent field procedure. The
             infrastructure provides access to QM/MM models that may serve as the
             foundation for QM/MM-{\ensuremath{\Delta}}MLP potentials, which
             supplement the semiempirical QM/MM model with a MLP correction trained
             to reproduce ab{~}initio QM/MM energies and forces. Efficient
             optimization of minimum free energy pathways is enabled through a new
             surface-accelerated finite-temperature string method implemented in
             the FE-ToolKit package. Furthermore, we interfaced Sander with the
             i-PI software by implementing the socket communication protocol used
             in the i-PI client-server model. The new interface with i-PI allows
             for the treatment of nuclear quantum effects with semiempirical
             QM/MM-{\ensuremath{\Delta}}MLP models. The modular interoperable
             software is demonstrated on proton transfer reactions in guanine-
             thymine mispairs in a B-form deoxyribonucleic acid helix. The current
             work represents a considerable advance in the development of modular
             software for performing free energy simulations of chemical reactions
             that are important in a wide range of applications.},
}