Boris Kozinsky

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.

Jul 19, 2023 | nature.com | Boris Kozinsky

Deep learning has the potential to accelerate atomistic simulations, but existing models suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert Musaelian, and Boris Kozinsky outline how exploiting the symmetry of Euclidean space offers a new way to address these challenges. Elucidating the behaviour of molecules and materials at the atomic scale is a core goal throughout the physical sciences.

Mar 23, 2023 | link.aps.org | Zachary Goodwin |Michael McEldrew |Massachusetts USA |Boris Kozinsky

Abstract Conventional lithium-ion batteries, and many next-generation technologies, rely on organic electrolytes with multiple solvents to achieve the desired physicochemical and interfacial properties. The complex interplay between these properties can often be elucidated via the coordination environment of the cation. We develop a theory for the coordination shell of cations in nonaqueous solvent mixtures that can be applied with high fidelity, up to extremely high salt concentrations.