计算机科学
透视图(图形)
光学(聚焦)
数据科学
机器学习
钥匙(锁)
人工智能
分子动力学
人机交互
管理科学
化学
物理
工程类
计算机安全
光学
计算化学
作者
Yihang Wang,João Marcelo Lamim Ribeiro,Pratyush Tiwary
出处
期刊:Cornell University - arXiv
日期:2019-01-01
标识
DOI:10.48550/arxiv.1909.11748
摘要
Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated in running even a microsecond long MD simulation human comprehensible. Second, how to efficiently sample the underlying free energy surface and kinetics. In this short perspective, we summarize machine learning based ideas that are solving both of these limitations, with a focus on their key theoretical underpinnings and remaining challenges.
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