70 citations · 73 across the 4 of their papers we have counts for
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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-Å, a machine learning interatomic potential intended to replace DFT in crystal structure prediction (CSP). We decompose the total energy into s…
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
William J. Baldwin, Ilyes Batatia, Martin Vondrák +2
Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectu…
Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields
Ilyes Batatia, Chen Lin, Joseph Hart +5
Creating a single unified interatomic potential capable of attaining ab initio accuracy across all chemistry remains a long-standing challenge in computational chemistry and materi…
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith +20
Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of thi…