基于均力匹配的可扩展粗粒化
Scaling Transferable Coarse-graining with Mean Force Matching
AI总结：
本文提出均力匹配方法，通过减少训练样本和模拟时间，提升粗粒化模型的准确性和可转移性。
摘要：
粗粒分子动力学常常为了计算效率而牺牲准确性和可转移性，但使用机器学习势能函数使粗粒模型的性能可与原子分子动力学相媲美。然而，由于广泛使用的
英文摘要：
Coarse-grained molecular dynamics often sacrifices accuracy and transferability for computational efficiency, but the use of machine learned potentials is helping coarse-grained models attain performance on par with atomistic molecular dynamics. Nevertheless, developing representations of the coarse-grained potential energy surface faces severe scaling challenges due to the extreme data demands of widely used "bottom-up" coarse-graining objectives. In this work, we show that mean force matching, a strategy for training thermodynamically consistent coarse-grained models, requires 50x fewer training samples and 87% less total atomistic simulation time, while obtaining better accuracy on the potential of mean force for unseen proteins compared to other commonly used objectives. By systematically removing noise from the objective function, we demonstrate that it is possible to scale machine learning architectures for coarse-graining, enabling highly accurate and transferable models. We show the advantages of mean force matching both theoretically and through exhaustive benchmarking using thermodynamic consistency as the primary metric of accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
