Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Disordered structures, characterized by their lack of periodicity, present significant challenges in fields such as materials science and biology. Conventional methods often fall short of capturing the intricate properties and behaviors of these complex systems. For example, the prediction of material properties in amorphous polymers and high‐entropy alloys has historically been inaccurate due to their inherent disorder, which arises from the probabilistic nature of structural defects and nonuniform atomic arrangements. However, the rise of machine learning (ML) offers a revolutionary approach to understanding and predicting the behavior of disordered materials. This perspective article explores how ML techniques, including neural networks and generative models, provide unprecedented insights into materials with inherent disorder, driving advances in industries such as energy storage, drug discovery, and structural engineering. By leveraging powerful algorithms, researchers can now predict structural properties, identify hidden patterns, and accelerate the discovery of novel materials. Case studies illustrate the ability of ML to overcome data scarcity, enhance model reliability, and enable real‐time analysis of disordered structures. While challenges such as data quality and computational costs remain, the integration of ML with traditional methods marks a transformative leap in our ability to navigate the disordered landscape, setting the stage for ground‐breaking discoveries.