249 citations · 260 across the 7 of their papers we have counts for
4 papers · 1 filter
Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models
Steffen Wedig, Felix Burton, Rokas Elijošius +2
Foundation machine-learned interatomic potentials (MLIPs) are trained on large quantum-mechanical datasets and generalise across broad regions of chemical and configurational space…
Multi-head committees enable direct uncertainty prediction for atomistic foundation models
Hubert Beck, Pavol Simko, Lars L. Schaaf +2
Machine learning potentials have become a standard tool for atomistic materials modelling. While models continue to become more generalisable, an open challenge relates to efficien…
BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
Lars L. Schaaf, Ilyes Batatia, Christoph Brunken +2
Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning forc…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…