A physics informed machine learning model predicts thermal conductivity from infrared images in milliseconds, enabling fast, ...

Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...

The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...

Based on these challenges, a comprehensive reassessment of how AI should be deployed in electrocatalysis has become urgently needed. Addressing this need, a review published (DOI: 10.1016/j.esci.2025.

The Nobel Prize in Physics was awarded to US scientist John Hopfield and British-Canadian researcher Geoffrey Hinton for their work in the field of machine learning, the Royal Swedish Academy of ...

US scientist John Hopfield and British-Canadian researcher Geoffrey Hinton have won the Nobel Prize in Physics for creating the "building blocks of machine learning," the Royal Swedish Academy of ...

Two scientists have been awarded the Nobel Prize in Physics “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” John Hopfield, an emeritus ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made ...

Researchers employ machine learning to more accurately model the boundary layer wind field of tropical cyclones. Conventional approaches to storm forecasting involve large numerical simulations run on ...