Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials

TL;DR

This review discusses the current state and future challenges in simulating amorphous materials, emphasizing the integration of advanced computational techniques like machine learning to bridge gaps with experimental data.

Contribution

It highlights recent advances such as MLIPs and generative models, proposing new directions to improve simulation accuracy and scalability for amorphous materials.

Findings

Current computational methods have limitations in time and length scales.

Machine learning techniques are promising for modeling amorphous materials.

Bridging simulation and experiment remains a key challenge.

Abstract

Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique nouveau (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter, e.g., the Morphological Autoregressive Protocol (MAP). Examples are drawn from the amorphous silicon and silica literature as well as from molecular glasses. Our outlook stresses the need for new computational methods to extend the time- and length- scales accessible through numerical simulations.