2 citations · 2 across the 8 of their papers we have counts for
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Transferable machine learning of excited-state dynamics with extremal pooling
Cesare Malosso, Wei Bin How, Gonzalo Díaz Mirón +2
Photochemical processes govern phenomena ranging from solar energy conversion and atmospheric chemistry to vision and photosynthesis. Accurate simulation of these processes require…
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
Matthias Kellner, Teitur Hansen, Thomas Bligaard +2
Machine-learning models of atomic-scale interactions achieve the accuracy of the quantum mechanical calculations on which they are trained, but at a dramatically lower computationa…
Learning Long-Range Representations with Equivariant Messages
Egor Rumiantsev, Marcel F. Langer, Tulga-Erdene Sodjargal +2
Machine learning interatomic potentials trained on first-principles reference data are becoming valuable tools for computational physics, biology, and chemistry. Equivariant messag…