On the robust extrapolation of high-dimensional machine learning potentials

We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in regions of the representation space which lie outside the convex hull defined by the training set points. We then propose a perspective to rationalise the domain of robust extrapolation and accurate prediction of atomistic machine learning potentials in terms of the probability density induced by training points in the representation space.
The data here contained can be used to reproduce all results and graphs shown in the article. We also include the trajectory files for the Au13 dataset we generate by running molecular dynamics simulations of an Au nanoparticle containing 13 atoms at temperatures of 50K, 100K, 200K, 300K, and 400K. Details regarding the generation of such dataset can be found in the supplementary information file for the article.

本研究表明，与主流假设相悖，基于高维原子密度表征（high-dimensional atom-density representations）构建的机器学习势（machine learning potentials），其预测结果几乎仅出现在由训练集点所定义的表征空间凸包（convex hull）之外的区域。随后，我们提出了一种新视角，可通过表征空间中训练点诱导的概率密度，对原子级机器学习势的稳健外推域与精准预测域进行合理化阐释。本文附带的数据集可用于复现文章中展示的全部结果与图表。此外，我们还提供了Au13数据集的轨迹文件：该数据集通过对含13个金原子的金纳米颗粒在50K、100K、200K、300K及400K温度下开展分子动力学模拟生成。关于该数据集的生成细节，可查阅文章的补充信息文件。