SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion
Daniil Poletaev, Artem Oganov

TL;DR
This paper introduces a method combining machine learning potentials with SSCHA to predict crystal structures at finite temperatures considering quantum effects, demonstrated on LaH10 under high pressure.
Contribution
It integrates MLIPs with SSCHA for evolutionary CSP at finite temperatures, accounting for quantum anharmonic effects, and compares active-learning and universal MLIPs for accuracy.
Findings
Quantum anharmonicity simplifies free-energy landscapes.
MLIPs enable efficient structure prediction with quantum effects.
Correct stability rankings require quantum anharmonic considerations.
Abstract
Accurate crystal structure prediction (CSP) at finite temperatures with quantum anharmonic effects remains challenging but very prominent in systems with lightweight atoms such as superconducting hydrides. In this work, we integrate machine-learned interatomic potentials (MLIPs) with the stochastic self-consistent harmonic approximation (SSCHA) to enable evolutionary CSP on the quantum anharmonic free-energy landscape. Using LaH at 150 GPa and 300 K as a test case, we compare two approaches for SSCHA-based CSP: using light-weight active-learning MLIPs (AL-MLIPs) trained on-the-fly from scratch, and foundation models or universal MLIPs (uMLIPs) from the Matbench project. We demonstrate that AL-MLIPs allow to correctly predict the experimentally known cubic Fmm phase as the most stable polymorph at 150 GPa but require corrections within the thermodynamic perturbation…
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Taxonomy
TopicsMachine Learning in Materials Science · Quantum many-body systems · Quantum, superfluid, helium dynamics
