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 ...

A physics-informed deep learning model, PBCNet, is proposed for predicting the relative binding affinity of ligands in order to improve guiding structure-based drug lead optimization.

A research team has used a machine learning approach to investigate the evolution of shell structure for nuclei far from the stability valley. The study, published in Physics Letters B and ...

In contrast, the author's machine learning algorithm is equipped with atmospheric physics equations that can produce more accurate results faster and with less data.

Another advantage of modeling diffusion using kinosons and machine learning is that it is significantly faster than calculating long-timescale, whole trajectories.

John Hopfield and Geoffrey Hinton used tools from physics to develop data analysis methods that underlie machine learning.

Physicists have been using machine learning for decades, says Nhan Tran, a physicist at Fermi National Accelerator Laboratory and the coordinator of the Fermilab AI project and the Fast Machine ...

By combining a new way of modeling quantum systems with increasingly sophisticated machine learning algorithms, researchers have established a method for classical machines to model and predict ...

Princeton Plasma Physics Laboratory is marking significant results from the lab’s artificial intelligence machine learning research.