3 papers
physics.chem-ph2026
DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
Laurence I. Midgley, Chen Lin, J. Harry Moore +8
We present an evaluation of CSP-MACE-Ã , a machine learning interatomic potential intended to replace DFT in crystal structure prediction (CSP). We decompose the total energy into…
physics.chem-ph2025
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…
physics.chem-ph2025
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning +8
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dy…