249 citations · 260 across the 7 of their papers we have counts for
7 papers
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…
Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
Terry C. W. Lam, Niamh O'Neill, Christoph Schran +1
The accuracy of machine learning interatomic potentials suffers from reference data that contains numerical noise. Often originating from unconverged or inconsistent electronic-str…
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…
Implicit Neural Representations for Chemical Reaction Paths
Kalyan Ramakrishnan, Lars L. Schaaf, Chen Lin +2
We show that neural networks can be optimized to represent minimum energy paths as continuous functions, offering a flexible alternative to discrete path-search methods such as Nud…
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…
Equivariant Matrix Function Neural Networks
Ilyes Batatia, Lars L. Schaaf, Huajie Chen +3
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MP…