New model makes machine learning potentials more accurate and more accessible

A study in Nature Communications by Michele Ceriotti’s group at EPFL has introduced a new dataset and model that greatly improve the efficiency of machine-learning interatomic potentials (MLIPs) and ...
mccormick.northwestern.edu

New Framework Applies Machine Learning to Atomistic Modeling

Northwestern Engineering researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials — the guiding rules describing how atoms interact — in ...
Royal Society of Chemistry

History (and future) of modelling materials using interatomic potentials

Interatomic Potentials and modelling as a tool in materials science – Prof Sir Richard Catlow, Dept. of Chemistry, UCL; School of Chemistry, Cardiff University; UK Catalysis Hub, Research Complex at ...
mccormick.northwestern.edu

Atomistic Experiments and Machine-Learning-Inspired Modeling Lead to Better Understanding of 2D Materials

The global market for 2D materials — already estimated at several billion dollars annually — is growing at a 4 percent rate. This is explained by the importance of these newly synthesized materials, ...
NextBigFuture

Researchers Accurately Simulate 100 Million Atoms With Machine Learning

Harvard researchers bring the accuracy, sample efficiency, and robustness of deep equivariant neural networks to the simulate 44 million atoms. This is achieved through a combination of innovative ...