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 significant strides in computational chemistry. They have achieved a major ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

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

Orbital-free approach enables precise, stable, and physically meaningful calculation of molecular energies and electron densities By applying new methods of machine learning in quantum chemistry ...

A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in complex two-dimensional (2D ...

Muons tend to scatter more from high-atomic-number materials, so the technique is particularly sensitive to the presence of materials such as uranium. As a result, it has been used to create systems ...

A particle collision reconstructed using the new CMS machine-learning-based particle-flow (MLPF) algorithm. The HFEM and HFHAD signals come from the ...

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

Design engineers can use AI-driven simulation to overcome bottlenecks and accelerate materials discovery with hybrid workflow approaches.

Based on these challenges, a comprehensive reassessment of how AI should be deployed in electrocatalysis has become urgently ...