计算机科学
观点
采样(信号处理)
强化学习
领域(数学)
降维
数据科学
人工智能
维数之咒
接口(物质)
机器学习
领域(数学分析)
数据挖掘
数学
滤波器(信号处理)
计算机视觉
艺术
数学分析
气泡
最大气泡压力法
并行计算
纯数学
视觉艺术
作者
Mehdi Shams,Zachary A. Smith,Lukas Herron,Ziyue Zou,Pratyush Tiwary
标识
DOI:10.1146/annurev-physchem-083122-125941
摘要
Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe timescale limitations. To address this, enhanced sampling methods have been developed to improve the exploration of configurational space. However, implementing these methods is challenging and requires domain expertise. In recent years, integration of machine learning (ML) techniques into different domains has shown promise, prompting their adoption in enhanced sampling as well. Although ML is often employed in various fields primarily due to its data-driven nature, its integration with enhanced sampling is more natural with many common underlying synergies. This review explores the merging of ML and enhanced MD by presenting different shared viewpoints. It offers a comprehensive overview of this rapidly evolving field, which can be difficult to stay updated on. We highlight successful strategies such as dimensionality reduction, reinforcement learning, and flow-based methods. Finally, we discuss open problems at the exciting ML-enhanced MD interface.
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