2019/07/12 by Malthe K. Bisbo, Malthe Kjær Bisbo, Bjørk Hammer · 169 citations
Computer Science · Materials Science · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Artificial intelligence #Computational Drug Discovery Methods #Computer science #Machine Learning in Materials Science #Machine learning #Surrogate model #cond-mat.mtrl-sci #physics.chem-ph
paper · pdf · doi:10.1103/physrevlett.124.086102
published in Physical Review Letters 124(8), 086102 (American Physical Society)
arxiv created 2019/07/12 · openalex publication_date 2020/02/27 · arxiv updated 2020/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We propose a scheme for global optimization with first-principles energy expressions of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a number of local relaxations (exploitation) and further structural searches (exploration). Assuming Gaussian processes, deploying two separate kernel widths to better capture rough features of the energy landscape while retaining a good resolution of local minima, an acquisition function is used to decide on which of the resulting structures is the more promising and should be treated at the first-principles level. The method is demonstrated to outperform by 2 orders of magnitude a well established first-principles based evolutionary algorithm in finding surface reconstructions. Finally, global optimization with first-principles energy expressions is utilized to identify initial stages of the edge oxidation and oxygen intercalation of graphene sheets on the Ir(111) surface.