Machine learning of atomic-scale properties is revolutionizing molecular
modelling, making it possible to evaluate inter-atomic potentials with
first-principles accuracy, at a fraction of the costs. The accuracy, speed and
reliability of machine-learning potentials, however, depends strongly on the
way atomic configurations are represented, i.e. the choice of descriptors used
as input for the machine learning method. The raw Cartesian coordinates are
typically transformed in "fingerprints", or "symmetry functions", that are
designed to encode, in addition to the structure, important properties of the
potential-energy surface like its invariances with respect to rotation,
translation and permutation of like atoms. Here we discuss automatic protocols
to select a number of fingerprints out of a large pool of candidates, based on
the correlations that are intrinsic to the training data. This procedure can
greatly simplify the construction of neural network potentials that strike the
best balance between accuracy and computational efficiency, and has the
potential to accelerate by orders of magnitude the evaluation of Gaussian
Approximation Potentials based on the Smooth Overlap of Atomic Positions
kernel. We present applications to the construction of neural network
potentials for water and for an Al-Mg-Si alloy, and to the prediction of the
formation energies of small organic molecules using Gaussian process
regression.

本文介绍了一种自动选择指纹的协议，以便构建既能够在精度和计算效率之间实现最佳平衡，又能够加速高斯近似势的评估的神经网络势。其中展示了应用于水和 Al-Mg-Si 合金的神经网络势构建，并使用高斯过程回归预测小有机分子的形成能。

机器学习势使用的原子指纹和参考构型的自动选择

Automatic Selection of Atomic Fingerprints and Reference Configurations  for Machine-Learning Potentials

Determining the stability of molecules and condensed phases is the
cornerstone of atomistic modelling, underpinning our understanding of chemical
and materials properties and transformations. Here we show that a machine
learning model, based on a local description of chemical environments and
Bayesian statistical learning, provides a unified framework to predict
atomic-scale properties. It captures the quantum mechanical effects governing
the complex surface reconstructions of silicon, predicts the stability of
different classes of molecules with chemical accuracy, and distinguishes active
and inactive protein ligands with more than 99% reliability. The universality
and the systematic nature of our framework provides new insight into the
potential energy surface of materials and molecules.

本研究基于局部化学环境和贝叶斯统计学习的机器学习模型，提供了一个统一的框架，可以预测原子尺度的稳定性和不同类别的分子的化学精度，可以区分活性和非活性的蛋白质配体，这种方法可以对材料和分子的势能面提供新的洞察。