Computational Approaches for the Discovery of New Phase-Change Materials Using Two Method Machine Learning and First-Principles Density Functional Theory
TL;DR
This work evaluates two approaches for solid-solid PCM discovery: data-driven machine-learning screening and first-principles density functional theory (DFT) modelling, highlighting the complementary roles of ML for rapid candidate identification via screening of known PCMs and DFT for mechanistic characterisation.
Abstract
Solid-solid phase-change materials (PCMs) are attractive for thermal energy storage due to the absence of leakage and their suitability for compact and safe storage systems. However, the discovery of new solid-solid PCMs with targeted transition temperatures and high latent heat remains challenging. This work evaluates two approaches for solid-solid PCM discovery: data-driven machine-learning (ML) screening and first-principles density functional theory (DFT) modelling. ML screening identified PEG-1000-glycerol mixtures as promising candidates near room temperature, with experimental validation confirming composition-dependent solid-solid transitions (between 14.10 to 31.40 °C) absent in the pure components. Furthermore, first-principles DFT calculations, implemented using Quantum ESPRESSO, reveal low-frequency phonon instabilities in a candidate PCM (2,2-dimethylpropane-1,3-diol (DMPD)) molecular crystal, providing atomistic insight into lattice-driven phase-transition mechanisms. Together, the results highlight the complementary roles of ML for rapid candidate identification via screening of known PCMs and DFT for mechanistic characterisation, supporting a more efficient pipeline for developing solid-solid PCMs for low-temperature thermal energy storage in decarbonised energy systems.