Efficient Modelling of Anharmonicity and Quantum Effects in PdCuH$_2$ with Machine Learning Potentials
Francesco Belli, Eva Zurek

TL;DR
This paper introduces a machine learning-based protocol combined with SSCHA to efficiently model quantum effects and anharmonicity in materials, demonstrated on PdCuH$_x$ compounds, significantly reducing computational costs.
Contribution
The authors develop a new method pairing machine learning potentials with SSCHA, enabling large-scale, cost-effective quantum and anharmonicity modeling in complex materials.
Findings
Identified a stable PdCuH$_2$ structure relevant to superconductivity.
Reduced SSCHA computational expense by approximately 96%.
Demonstrated the method's potential for routine quantum effects inclusion in materials discovery.
Abstract
Quantum nuclear effects and anharmonicity impact a wide range of functional materials and their properties. One of the most powerful techniques to model these effects is the Stochastic Self-Consistent Harmonic Approximation (SSCHA). Unfortunately, the SSCHA is extremely computationally expensive, prohibiting its routine use. We propose a protocol that pairs machine learning interatomic potentials, which can be tailored for the system at hand via active learning, with the SSCHA. Our method leverages an upscaling procedure that allows for the treatment of supercells of up to thousands of atoms with practically minimal computational effort. The protocol is applied to PdCuH () compounds, chosen because previous experimental studies have reported superconducting critical temperatures, s, as high as 17~K at ambient pressures in an unknown hydrogenated PdCu phase. We…
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Taxonomy
TopicsAdvanced Physical and Chemical Molecular Interactions · Machine Learning in Materials Science · Quantum Computing Algorithms and Architecture
