Topic: experimental validation

This MRS workshop, developed with the Japan Society of Applied Physics, examines how AI can advance autonomous materials research beyond property prediction. Sessions connect physics-informed models, fundamental physical understanding, experimental validation and human–AI collaboration. Particular attention goes to data-efficient learning where experimental datasets are small or difficult to obtain. The online programme runs 4 November, 10:00–13:00, and 5 November, 19:00–22:00, US Eastern Time (EST). Registered participants receive session recordings. Registration is USD 100 regular, USD 75 fo

Advance superconducting accelerator magnets within the Berkeley Center for Magnet Technology and Superconducting Magnet Program. Research includes high- and low-temperature superconductors, multiphysics models of cables and magnets, subscale coils and experimental validation of new geometries. The role combines design, fabrication, testing and performance analysis with scientists and engineers. The initial career-track term is two years, with targeted monthly pay of USD 10,389–14,544.

Surya Ganguli and Bowen Liu join Vijay Pande to examine how machine learning can support biological discovery and drug development. They compare physics-based modelling, learned molecular representations and self-supervised foundation models; discuss protein structures, target identification and generative molecule design; and explain why experimental validation and generalisation beyond training data remain difficult. The conversation also considers interpretable cellular representations and the potential role of AI in clinical-trial recruitment and prediction. The publisher provides an accom