Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction

TL;DR

This study evaluates the effectiveness of a universal machine-learning interatomic potential, M3GNet, in accelerating crystal structure prediction for complex quaternary oxides, successfully rediscovering known materials and identifying new stable compounds.

Contribution

The paper demonstrates the application of uMLIP (M3GNet) in accelerating CSP for complex materials, discovering new stable compounds and highlighting current limitations in search algorithms.

Findings

uMLIP can rediscover known materials absent from training data

Seven new thermodynamically stable compounds identified

Stability predictions require validation with higher-level methods

Abstract

Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However, their effectiveness in identifying new, complex materials remains uncertain. Here, we systematically assess the capability of a uMLIP (i.e.,M3GNet) to accelerate CSP in quaternary oxides. Through extensive exploration of the Sr-Li-Al-O and Ba-Y-Al-O systems, we show that uMLIP can rediscover experimentally known materials absent from its training set and identify seven new thermodynamically and dynamically stable compounds. These include a new polymorph of Sr2LiAlO4 (P3221) and a new disordered phase, Sr2Li4Al2O7 (P1_bar). Furthermore, our results show stability predictions based on the semilocal PBE functional require cross-validation with higher-level methods, such as SCAN and RPA, to ensure reliability. While uMLIPs substantially reduce the computational cost of CSP, the primary bottleneck has shifted to the efficiency of search algorithms in navigating complex structural spaces. This work highlights both the promise and current limitations of uMLIP-driven CSP in the discovery of new materials.