Simon Batzner

Jan 14, 2025 | nature.com | Simon Batzner |Boris Kozinsky

AbstractMolecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years.

Mar 6, 2023 | nature.com | Simon Batzner

A biasing potential is derived from the uncertainty of a neural network ensemble and used to modify the potential energy surface in molecular dynamics simulations and facilitate the determination of underrepresented structural regions. Molecular dynamics simulations have proven to be of tremendous value across chemistry, materials science, physics, and biology, helping scientists gain atomistic understanding of complex processes.

Feb 3, 2023 | nature.com | Simon Batzner |Anders F Johansson |Mordechai Kornbluth |Cameron J. Owen

AbstractA simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy.