Best I R, Sullivan T J, Kermode J R
Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom.
Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom.
J Chem Phys. 2024 Aug 14;161(6). doi: 10.1063/5.0214590.
Atomistic simulations often rely on interatomic potentials to access greater time and length scales than those accessible to first-principles methods, such as density functional theory. However, since a parameterized potential typically cannot reproduce the true potential energy surface of a given system, we should expect a decrease in accuracy and increase in error in quantities of interest calculated from these simulations. Quantifying the uncertainty on the outputs of atomistic simulations is thus an important, necessary step so that there is confidence in the results and available metrics to explore improvements in said simulations. Here, we address this research question by forming ensembles of atomic cluster expansion potentials, and using conformal prediction with ab initio training data to provide meaningful, calibrated error bars on several quantities of interest for silicon: the bulk modulus, elastic constants, relaxed vacancy formation energy, and the vacancy migration barrier. We evaluate the effects on uncertainty bounds using a range of different potentials and training sets.
原子模拟通常依赖原子间势来获取比第一性原理方法（如密度泛函理论）所能达到的更大的时间和长度尺度。然而，由于参数化势通常无法再现给定系统的真实势能面，我们应该预期从这些模拟计算出的感兴趣量的精度会降低，误差会增加。因此，量化原子模拟输出的不确定性是一个重要且必要的步骤，这样才能对结果有信心，并利用可用的指标来探索改进这些模拟。在这里，我们通过构建原子团簇展开势的系综，并使用具有从头算训练数据的共形预测，来为硅的几个感兴趣量提供有意义的、经过校准的误差条：体模量、弹性常数、弛豫空位形成能和空位迁移势垒。我们使用一系列不同的势和训练集来评估对不确定性界限的影响。
