Data-driven learning and prediction of inorganic crystal structures

Abstract

Crystal structure prediction algorithms, including ab initio random structure searching (AIRSS), are intrinsically limited by the huge computational cost of the underlying quantum-mechanical methods. We have recently shown that a novel class of machine learning (ML) based interatomic potentials can provide a way out: by performing a high-dimensional fit to the ab initio energy landscape, these potentials reach comparable accuracy but are orders of magnitude faster. In this paper, we develop our approach, dubbed Gaussian approximation potential-based random structure searching (GAP-RSS), towards a more general tool for exploring configuration spaces and predicting structures. We present a GAP-RSS interatomic potential model for elemental phosphorus, which identifies and correctly “learns” the orthorhombic black phosphorus (A17) structure without prior knowledge of any crystalline allotropes. Using the tubular structure of fibrous phosphorus as an example, we then discuss the limits of free searching, and discuss a possible way forward that combines a recently proposed fragment analysis with GAP-RSS. Examples of possible tubular (1D) and extended (3D) hypothetical allotropes of phosphorus as found by GAP-RSS are discussed. We believe that in the future, ML potentials could become versatile and routine computational tools for materials discovery and design.