Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...

A study in Nature Communications by Michele Ceriotti’s group at EPFL has introduced a new dataset and model that greatly improve the efficiency of machine-learning interatomic potentials (MLIPs) and ...

Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

Machine Learning (ML) and Artificial Intelligence (AI) have become essential technologies across industries, automating tasks at a speed and scale far beyond human capabilities. However, building ...

Catalog description: Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of ...