(Nanowerk News) Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of ...

Machine learning-based neural network potentials often cannot describe long-range interactions. Here the authors present an approach for building neural network potentials that can describe the ...

Researchers at Stony Brook University’s Materials Science and Chemical Engineering Department have been using computers that are capable of learning to recognize various steps in the complex movement ...

Flow chemistry engineering specialists Vapourtec is working with Accelerated Materials to develop the scope and potential of machine learning (ML) in flow chemistry. Machine learning delivers a number ...

In March, a paper in the Journal of the American Chemical Society sparked a heated Twitter debate on the value of machine learning for predicting optimal reaction pathways in synthetic chemistry. The ...

Machine-learning tools have taken us closer to understanding electrons and how they behave in chemical interactions, following news that UK-based AI company DeepMind, owned by Google’s parent company ...

Chemistry deals with that most fundamental subject: matter. New drugs, materials and batteries all depend on our ability to ...

Imagine you’re a materials scientist and your job is to discover a new material, a combination of atoms no one has ever made. Maybe you’re looking for a metal-organic framework (MOF). They have a lot ...