AI RESEARCH
TriForces: Augmenting Atomistic GNNs for Transferable Representations
arXiv CS.LG
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ArXi:2605.20581v1 Announce Type: new Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in practice, they must often be adapted to target chemistries using small and expensive task-specific datasets. However, MLIPs transfer inconsistently across domains, with representations that often loose accessible composition and structure information.