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.

翻译：理解材料表面与界面在催化或电子学等应用中至关重要。结合电子结构能量与统计力学的从头算模拟，原则上可以预测材料表面作为热力学变量函数的结构。然而，当需要统计采样的巨大相空间与精确能量模拟相结合时，其代价过于高昂。本文提出了一种双管齐下的计算循环，用于预测多组分材料的表面相图，该方法同时加速了能量评分和统计采样过程。通过闭环主动学习，基于高通量密度泛函理论计算训练出快速、可扩展且数据高效的机器学习原子间势能。利用虚拟表面位点实现了半巨正则系综中的马尔可夫链蒙特卡洛采样。对GaN(0001)和SrTiO3(001)表面的预测结果与先前研究一致，表明该策略能够模拟复杂材料表面并发现先前未报道的表面终端结构。