EPJ Plus Focus Point Issue: Machine Learning for Materials Physics: From Pitfalls to Best Practices

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Published on 02 September 2025

Guest Editors: Domenico Di Sante and Anirvan M. Sengupta

The fusion of machine learning (ML) and materials science is opening unprecedented opportunities in research and innovation. As traditional methods struggle to face the complexity of modern materials and their vast datasets, ML is intervening to accelerate discovery, optimize properties, and shed light on intricate phenomena.

In the Focus Point "Machine Learning for Materials Physics: From Pitfalls to Best Practices" six studies showcase how ML is permeating this field. From modeling quantum many-body systems to predicting new superconducting materials, these papers highlight how ML algorithms are driving efficiency, enhancing precision, and offering new possibilities. This Focus Point also addresses key challenges, such as interpretability and scalability, highlighting the need for interdisciplinary collaboration between ML experts and materials scientists.

Dive into this special issue to explore the cutting-edge innovations reshaping materials science—and see how ML is revolutionizing our understanding of the physical world.

All articles are available here and are freely accessible until 31 October 2025. For further information, read the Editorial.