期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 115, 期 41, 页码 10251-10256出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1811056115
关键词
crystal structure prediction; polymorphism; enhanced sampling; molecular simulation; urea
资金
- NCCR MARVEL - Swiss National Science Foundation
- European Union [ERC-2014-AdG-670227/VARMET]
- Swiss National Supercomputing Center (CSCS) [mr3]
We introduce a computational method to discover polymorphs in molecular crystals at finite temperature. The method is based on reproducing the crystallization process starting from the liquid and letting the system discover the relevant polymorphs. This idea, however, conflicts with the fact that crystallization has a timescale much longer than that of molecular simulations. To bring the process within affordable simulation time, we enhance the fluctuations of a collective variable by constructing a bias potential with well-tempered metadynamics. We use as a collective variable an entropy surrogate based on an extended pair correlation function that includes the correlation between the orientations of pairs of molecules. We also propose a similarity metric between configurations based on the extended pair correlation function and a generalized Kullback-Leibler divergence. In this way, we automatically classify the configurations as belonging to a given polymorph, using our metric and a hierarchical clustering algorithm. We apply our method to urea and naphthalene. We find different polymorphs for both substances, and one of them is stabilized at finite temperature by entropic effects.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据