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Accurate and Efficient Machine Learning Interatomic Potentials for Finite Temperature Modeling of Molecular Crystals by Della Pia, Flaviano; Shi, Benjamin X.; Kapil, Venkat; Zen, Andrea; Alfè, Dario; Michaelides, Angelos is a scholarly article available to read on EtoBox.

What is Accurate and Efficient Machine Learning Interatomic Potentials for Finite Temperature Modeling of Molecular Crystals about?

As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for the accurate and efficient calculation of sublimation enthalpies - a key thermodynamic quantity measuring the stability of a molecular crystal. Specifically, two key stumbling blocks are: (i) the need for thousands of ab initio quality reference structures to generate training data; and (ii) the sometimes unreliable nature of density functional theory, the main technique for generating such data. Exploiting recent developments in foundational models for chemistry and materials science alongside accurate quantum diffusion Monte Carlo benchmarks, offers a promising path forward. Herein, we demonstrate the generation of MLIPs capable of describing molecular crystals at finite temperature and pressure with sub-chemical accuracy, using as few as $\sim 200$ data structures; an order of magnitude improvement over the current state-of-the-art. We apply this framework to compute the sublimation enthalpies of the X23 dataset, accounting for anharmonicity and nuclear quantum effects, achieving sub-chemical accuracy w

Author

Della Pia, Flaviano; Shi, Benjamin X.; Kapil, Venkat; Zen, Andrea; Alfè, Dario; Michaelides, Angelos

Published

2025

Language

EN