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.

Machine learning (ML) is not only reshaping the tech industry but also making significant strides in environmental studies, ...

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

The 2024 Nobel Prize in physics has been awarded to John Hopfield and Geoffrey Hinton for their fundamental discoveries in machine learning, which paved the way for how artificial intelligence is ...

Machine learning can get a boost from quantum physics. On certain types of machine learning tasks, quantum computers have an exponential advantage over standard computation, scientists report in ...

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.

Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in ...

Machine learning wins physics Nobel Two researchers share 2024 prize for foundational work behind discoveries in artificial neural networks ...

Two pioneers behind machine learning — a technology that is already transforming society and that carries profound ethical questions — won the Nobel Prize in physics.

Scientists are now using classical machine learning to improve our understanding of quantum physics. AI algorithms can optimize the fine-tuning of quantum circuits, for example, which can prevent ...