The optimization of atomic structures plays a pivotal role in understanding and designing materials with desired properties. However, conventional computational methods often struggle with the formidable task of navigating the vast potential energy surface, especially in high-dimensional spaces with numerous local minima. Recent advancements in machine learning-driven surrogate models offer a promising avenue for alleviating this computational burden. In this study, we propose a novel approach that combines the strengths of universal machine learning potentials with a Bayesian approach using Gaussian processes. By using the machine learning potentials as priors for the Gaussian process, the Gaussian process has to learn only the difference between the machine learning potential and the target energy surface calculated for example by density functional theory. This turns out to improve the speed by which the global optimal structure is identified across diverse systems for a well-behaved machine learning potential. The approach is tested on periodic bulk materials, surface structures, and a cluster.

翻译：原子结构优化在理解和设计具有特定性质的材料中起着关键作用。然而，传统计算方法在探索广阔势能面时常常面临巨大挑战，尤其是在存在大量局部极小值的高维空间中。近期机器学习驱动的代理模型发展为缓解这一计算负担提供了有前景的途径。本研究提出一种创新方法，将通用机器学习势能与基于高斯过程的贝叶斯方法相结合。通过将机器学习势能作为高斯过程的先验分布，高斯过程仅需学习机器学习势能与目标能量面（例如通过密度泛函理论计算获得）之间的差异。结果表明，对于性能良好的机器学习势能，该方法能显著提升跨不同体系全局最优结构的识别速度。本方法在周期性体材料、表面结构及团簇体系上进行了验证。