论文标题
使用不确定性定量工具包扩展OpenKim，用于分子建模
Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling
论文作者
论文摘要
原子模拟是材料建模的重要工具。原子间电位（IP）是这种分子模型的核心，模型预测的准确性在很大程度上取决于IP的选择。不确定性定量（UQ）是评估原子模拟可靠性的新兴工具。原子质模型（OpenKim）的开放知识基础是一个网络基础设施项目，其目标是收集和标准化IPS研究以实现透明的可重复性研究。 OpenKim框架的一部分是Python软件包，基于KIM的学习集成拟合框架（KLIFF），它为IP中的参数拟合到数据提供了工具。本文向Kliff介绍了UQ工具箱扩展名。我们专注于两种不确定性来源：参数的变化和IP功能形式的不足。我们的实施使用平行扭曲的马尔可夫链蒙特卡洛（PTMCMC），调整采样温度以估算IP功能形式引起的不确定性。我们在Stillinger上演示 - 韦伯潜能可以预测钻石构型中硅的原子能量和力。最后，我们重点介绍了应用和使用这些工具的一些潜在细微之处，并建议从业者和IP开发人员使用这些工具。
Atomistic simulations are an important tool in materials modeling. Interatomic potentials (IPs) are at the heart of such molecular models, and the accuracy of a model's predictions depends strongly on the choice of IP. Uncertainty quantification (UQ) is an emerging tool for assessing the reliability of atomistic simulations. The Open Knowledgebase of Interatomic Models (OpenKIM) is a cyberinfrastructure project whose goal is to collect and standardize the study of IPs to enable transparent, reproducible research. Part of the OpenKIM framework is the Python package, KIM-based Learning-Integrated Fitting Framework (KLIFF), that provides tools for fitting parameters in an IP to data. This paper introduces a UQ toolbox extension to KLIFF. We focus on two sources of uncertainty: variations in parameters and inadequacy of the functional form of the IP. Our implementation uses parallel-tempered Markov chain Monte Carlo (PTMCMC), adjusting the sampling temperature to estimate the uncertainty due to the functional form of the IP. We demonstrate on a Stillinger--Weber potential that makes predictions for the atomic energies and forces for silicon in a diamond configuration. Finally, we highlight some potential subtleties in applying and using these tools with recommendations for practitioners and IP developers.
