Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

TL;DR

This paper introduces SPINNER, a machine learning-based framework that accelerates the prediction of stable inorganic crystal structures, enabling large-scale exploration beyond traditional computational limits.

Contribution

The paper presents SPINNER, a novel, empirically-free structure prediction method that uses neural network potentials to vastly speed up crystal structure searches compared to DFT.

Findings

Successfully predicts experimental or more stable phases for ~80% of tested materials.

Speeds up structure prediction by over 100 times relative to DFT.

Outperforms previous data mining and DFT-based methods in identifying stable phases.

Abstract

The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the computational crystal structure prediction is expected to mitigate this frustration, the NP-hardness and steep costs of density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a highly efficient and reliable structure-prediction framework based on exhaustive random searches and evolutionary algorithms, which is completely free from empiricism. Empowered by accurate neural network potentials, the program can navigate the configuration space faster than DFT by more than 10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected from the experimental database, SPINNER successfully identifies experimental (or theoretically more stable) phases for ~80% of materials within 5000 generations, entailing up to half a million structure evaluations for each composition. When benchmarked against previous data mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in the majority of cases. By developing a reliable and fast structure-prediction framework, this work opens the door to large-scale, unbounded computational exploration of undiscovered inorganic crystals.