Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Google Research（谷歌研究院）；Technische Universität Berlin（柏林工业大学）；University of Luxembourg（卢森堡大学）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, … 展开作者
Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Klaus-Robert Müller
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations, offering similar accuracy as ab initio methods at orders-of-magnitude speedup. Until now, MLFFs mainly capture short-range interactions in small molecules or periodic materials, due to the increased complexity of constructing models and obtaining reliable reference data for large molecules, where long-ranged many-body effects become important. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on "bottom-up" and "top-down" molecular fragments of varying size, from which the relevant physicochemical interactions can be learned. GEMS is applied to study the dynamics of alanine-based peptides and the 46-residue protein crambin in aqueous solution, allowing nanosecond-scale MD simulations of >25k atoms at essentially ab initio quality. Our findings suggest that structural motifs in peptides and proteins are more flexible than previously thought, indicating that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes such as protein (mis)folding, drug-protein binding, or allosteric regulation.
Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
NEC Laboratories Europe GmbH（NEC欧洲实验室）；NEC OncoImmunity AS（NEC OncoImmunity公司）；Institute for Artificial Intelligence, University of Stuttgart（人工智能研究所，斯图加特大学）
The dawn of alchemical free-energy methods in biomolecular simulations
Peking University（北京大学）；University of Rome “La Sapienza”（罗马第一大学）；IAC--CNR, Institute for the Application of Computing “M. Picone,” National Research Council（意大利国家研究委员会计算应用研究所（IAC-CNR））；University College Dublin（都柏林大学学院）；University of Chicago（芝加哥大学）
Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Asahi Kasei Pharma Corporation（旭化成制药株式会社）；University of California, Irvine（加州大学欧文分校）；University of California San Diego（加州大学圣迭戈分校）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Open Molecular Software Foundation（开放分子软件基金会）
PAL -- Parallel active learning for machine-learned potentials
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute of Theoretical Informatics（理论信息学研究所）；Institute of Nanotechnology（纳米技术研究所）；Institute for Physical Chemistry（物理化学研究所）
Quantum Thermodynamics of Electron Transport along Chains of Redox Centers
University of Lille（里尔大学）；Institut d’Électronique, de Microélectronique et de Nanotechnologie (IEMN CNRS UMR8520)（电子、微电子和纳米技术研究所）；Department of Chemical Systems Engineering, University of Tokyo（东京大学化学系统工程系）；Unité de Glycobiologie Structurale et Fonctionnelle (UGSF CNRS UMR8576)（结构与功能糖生物学单元）；CNRS（法国国家科学研究中心）；Centrale Lille（里尔中央理工学院）；Univ. Polytechnique Hauts-de-France（上法兰西工业大学）
Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction
Hamburg University of Technology（汉堡大学）；Centre for Quantum Materials and Technologies（量子材料与技术中心）；Helmholtz-Zentrum Hereon（海德堡研究中心）；Institute for Interface Physics and Engineering（界面物理与工程研究所）；Institute of Polymers and Composites（聚合物与复合材料研究所）
Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
Freie Universität Berlin（柏林自由大学）；Zuse School ELIZA（祖泽ELIZA学院）；Technische Universität Berlin（柏林工业大学）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Shanghai Jiao Tong University（上海交通大学）；Technische Universität München（慕尼黑工业大学）；Rice University（莱斯大学）；Microsoft Research AI4Science（微软研究院AI4Science）
