Understanding material surfaces and interfaces is vital in applications like
catalysis or electronics. Ab initio simulations, combining energies from
electronic structure with statistical mechanics, can, in principle, predict the
structure of material surfaces as a function of thermodynamic variables.
However, accurate energy simulations are prohibitive when coupled to the vast
phase space that must be statistically sampled. Here, we present a bi-faceted
computational loop to predict surface phase diagrams of multi-component
materials that accelerates both the energy scoring and statistical sampling
methods. Fast, scalable, and data-efficient machine learning interatomic
potentials are trained on high-throughput density-functional theory
calculations through closed-loop active learning. Markov-chain Monte Carlo
sampling in the semi-grand canonical ensemble is enabled by using virtual
surface sites. The predicted surfaces for GaN(0001) and SrTiO3(001) are in
agreement with past work and suggest that the proposed strategy can model
complex material surfaces and discover previously unreported surface
terminations.

使用机器学习插值位能函数和 Markov-chain Monte Carlo 采样方法，加快了多组分材料表面相图预测中的能量和统计抽样方法，可模拟复杂的材料表面并发现以前未报告的表面终止。

机器学习加速的模拟使得无启发式表面重建成为可能

Machine-learning-accelerated simulations enable heuristic-free surface  reconstruction

Improving the predictive capability of molecular properties in ab initio
simulations is essential for advanced material discovery. Despite recent
progress making use of machine learning, utilizing deep neural networks to
improve quantum chemistry modelling remains severely limited by the scarcity
and heterogeneity of appropriate experimental data. Here we show how training a
neural network to replace the exchange-correlation functional within a
fully-differentiable three-dimensional Kohn-Sham density functional theory
(DFT) framework can greatly improve simulation accuracy. Using only eight
experimental data points on diatomic molecules, our trained
exchange-correlation networks enable improved prediction accuracy of
atomization energies across a collection of 104 molecules containing new bonds
and atoms that are not present in the training dataset.

本文通过使用神经网络替代密度泛函理论中的交换 - 相关泛函来提高模拟精度，表明此方法可通过少量的实验数据点来处理包含新键和原子的化合物集合。（Keywords translated to: ab initio 模拟、机器学习、神经网络、量子化学建模、原子化能）