论文标题
分子动力学中的机器学习力场和粗粒变量：应用于材料和生物系统
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
论文作者
论文摘要
机器学习包括一组工具和算法，这些工具和算法现在在几乎所有科学和技术领域都变得流行。对于分子动力学也是如此，机器学习提供了从复杂系统模拟产生的大量数据中提取有价值信息的承诺。我们在这里审查了我们当前对原子系统计算研究的目标，利益和局限性的理解，重点是从AB-Initio数据库中构建经验力领域以及确定自由能量计算的反应坐标和增强采样的反应坐标。
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab-initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
