LAUSR

Thermal transport properties of amorphous materials are crucial for their emerging applications in energy and electronic devices. However, understanding and controlling thermal transport in disordered materials remains an outstanding challenge, owing to the intrinsic limitations of computational techniques and the lack of physically-intuitive descriptors for complex atomistic structures. Here, we show how combining machine learning-based models and experimental observations can help to accurately describe realistic structures, thermal transport properties, and structure-property maps for disordered materials, which is illustrated by a practical application on gallium oxide. First, we report the experimental evidence to demonstrate that machine learning interatomic potentials, generated in a self-guided fashion with minimum quantum-mechanical computations, enable the accurate modeling of amorphous gallium oxide and its thermal transport properties. Our atomistic simulations then reveal the microscopic changes in the short-range and medium-range order with density and elucidate how these changes can reduce localization modes and enhance coherences' contribution to heat transport. Finally, a physics-inspired structural descriptor for disordered phases is proposed, with which the underlying relationship between structures and thermal conductivities is predicted in a linear form. This work might shed light on the future accelerated exploration of novel thermal transport properties and mechanisms in disordered functional materials. This article is protected by copyright. All rights reserved.