Unraveling Thermal Transport Correlated with Atomistic Structures in Amorphous Gallium Oxide via Machine Learning Combined with Experiments

Abstract

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, it is shown 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, the experimental evidence is reported 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. The 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 may shed light on the future accelerated exploration of thermal transport properties and mechanisms in disordered functional materials.

Vibrational and thermal properties of amorphous alumina from first principles

“…In this section we summarize the salient features of the Wigner formulation of thermal transport [22], which describes heat conduction, accounting for the interplay between structural disorder, anharmonicity, and quantum Bose-Einstein statistics of atomic vibrations. This allows us to describe the thermal conductivity of solids ranging from crystals [23,36,37] to glasses [20,38].…”

Zeolite encapsulated organometallic complexes as model catalysts

“…[173][174][175][176] Machine learning based potentials (MLPs) have recently emerged as a promising method of accurately modelling the properties and dynamics of several systems and reactions. [177][178][179][180][181][182][183][184][185] As a result, MLPs are iteratively trained to 'learn' the potential energy surface of the system (based on limited DFT data) and can serve as a viable substitute for QM/MM and QM/QM schemes and apply to systems of arbitrary size at almost DFT level of accuracy. Consequently, MLPs can help to perform high-throughput screening of several catalyst configurations in multiple zeolites 179 if desired, in addition to being able to model system dynamics.…”

Patterned liquid metal embedded in brush-shaped polymers for dynamic thermal management

“…The structure of the polymer matrix and thermally conductive filler plays a pivotal role in determining the performance of their composite materials. 18,23,24 Importantly, the polymer molecular structure can be controlled by modifying the main chain structure, substituents, and crosslinking density. 19,25,26 By controlling these aspects, it becomes possible to lower the modulus of polymer-based TIMs, which is beneficial for reducing contact thermal resistance.…”

“…LM/polymer materials showcase superior flexibility and mechanical reliability in comparison with traditional rigid metal-polymer composites. 6,23,35,36 In the majority of the research studies, the distribution of LMs within a polymer matrix is achieved through the mechanical mixing of LMs and uncured polymers. For instance, Zhang et al 37 utilized 55% gallium-based LMs and 15% copper particles as fillers to make a composite with uncured Ecoflex, resulting in TIMs with a k value of 3.94 W m À1 K À1 and Young's modulus of 699 kPa.…”