Enabling molecular dynamics simulations of phase transformations in barocaloric layered materials with machine-learned force fields

Layered materials leveraging the order-disorder phase transition of hydrocarbon chains have been shown to exhibit significant barocaloric effects with promise for applications in thermal energy storage and conversion [1, 2]. While the material design space of barocaloric layered materials is large, a complete understanding of how layer structure, composition, and hydrocarbon chain length influence the barocaloric effect is currently lacking.



Using the machine-learned force field Allegro [3], we demonstrate how molecular dynamics simulations powered by E(3)-equivariant machine learning can accurately realize a fully atomistic description of barocaloric effects in multiple classes of layered materials. We provide new insights into the entropically driven phase-change and demonstrate our simulations’ good agreement with experiment, including data from quasi-elastic neutron scattering. The methods employed here move towards enabling the rational design of new and more performant materials for thermal energy management.



[1] J. Seo et al. Colossal barocaloric effects with ultralow hysteresis in two-dimensional metal–halide perovskites. Nat. Commun. 13 2536 (2022).

[2] J. Seo et al. Barocaloric effects in dialkylammonium halide salts. J. Am. Chem. Soc. 146 2736 (2024).

[3] A. Musaelian et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat. Commun. 14 579 (2023).