Population annealing is a powerful tool for large-scale Monte Carlo simulations. We adapt this method to molecular dynamics simulations and demonstrate its excellent accelerating effect by simulating the folding of a short peptide commonly used to gauge the performance of algorithms. The method is compared to the well established parallel tempering approach and is found to yield similar performance for the same computational resources. In contrast to other methods, however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus a unique tool that enables molecular dynamics to tap into the massively parallel computing power available in supercomputers that is so much needed for a range of difficult computational problems.
Simulated annealing: in mathematical global optimization computation, hybrid with local or global search, and practical applications in crystallography and molecular modelling
TRIDS: AI-native molecular docking framework for accelerating high-throughput virtual screening with physically valid binding poses
Shenzhen Bay Laboratory（深圳湾实验室）；Shenzhen medical academy of research and translation（深圳市医学科学院）；Peking University（北京大学）；Changping Laboratory（昌平实验室）；Westlake University（西湖大学）；Chromatin (Beijing) Technology Co., Ltd.（华因泰（北京）科技有限公司）；XtalPi Technologies Co., Ltd.（晶泰科技有限公司）；ICE Bioscience Inc.（冰鉴生物科技有限公司）
Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems
Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所凝聚态物理国家实验室）；School of Physical Sciences, University of Chinese Academy of Sciences（中国科学院大学物理科学学院）；Songshan Lake Materials Laboratory（松山湖材料实验室）
