A deep free energy model learns the SCHA free energy surface to enable high-throughput crystal structure prediction with finite-temperature and nuclear quantum effects, reproducing known La-Sc-H phases and discovering a new stable LaScH8 structure.
Journal of Physics: Condensed Matter , volume=
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TriForces adds a model-agnostic three-stream architecture plus self-supervised objectives to atomistic GNNs, improving transfer performance on MatBench, QM9, and limited-data OMat24 without DFT labels.
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Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning
A deep free energy model learns the SCHA free energy surface to enable high-throughput crystal structure prediction with finite-temperature and nuclear quantum effects, reproducing known La-Sc-H phases and discovering a new stable LaScH8 structure.
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TriForces: Augmenting Atomistic GNNs for Transferable Representations
TriForces adds a model-agnostic three-stream architecture plus self-supervised objectives to atomistic GNNs, improving transfer performance on MatBench, QM9, and limited-data OMat24 without DFT labels.