2.5k citations · 2.5k across the 3 of their papers we have counts for
3 papers
Enhancing molecular dynamics with equivariant machine-learned densities
Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving elect…
Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer +5
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent…
Efficient prediction of 3D electron densities using machine learning
Mihail Bogojeski, Felix Brockherde, Leslie Vogt-Maranto +4
The Kohn-Sham scheme of density functional theory is one of the most widely used methods to solve electronic structure problems for a vast variety of atomistic systems across diffe…