Barocaloric phase transformation from data efficient fine-tuning of machine learned interatomic potentials

Abstract

Solid-state cooling based on the barocaloric (BC) effect has emerged as promising environmentally friendly prospective alternative to conventional vapor-compression refrigeration. The search for suitable BC materials relies on efficient atomistic simulations of their phase behavior. Machine-learned interatomic potentials (MLIPs) enable such simulations at near density functional theory (DFT) accuracy, but generating the required DFT training data remains computationally demanding, which motivates development of strategies that reduce the amount of data needed to train accurate models. In this work, we use a prototypical BC material, ammonium sulfate, as a model system and investigate how small a training set can be while still reproducing the temperature-driven structural phase transformation that underlies its BC response. We train a series of MLIPs based on the MACE architecture using three strategies: training from scratch, and naive- and multihead replay fine-tuning of the MACE-MPA-0 foundation model. These strategies are evaluated on their ability to reproduce the phase transformation of ammonium sulfate in molecular dynamics simulations across a range of training-set sizes. We find that, while the MACE-MPA-0 foundation model itself fails to reproduce the transformation, and models trained from scratch break down for small datasets, fine-tuned models reproduce the transformation using as few as 5 to 10 60-atom DFT configurations. Both fine-tuning protocols yield similarly accurate results for ammonium sulfate, but we also find some indications that multihead replay fine-tuning is more robust on configurations outside the fine-tuning domain. Exploiting this data efficiency, we further show that models can be trained on small datasets and the hybrid-DFT level, and that some form of inclusion of dispersion correction is necessary to describe the phase behavior correctly.