An equivariant GNN trained on DFT energies of solvent-free PGNs reproduces those energies at low cost and drives MC simulations that match experimental structures despite training exclusively on out-of-equilibrium data.
Recent advances in metallic glasses.arXiv preprint arXiv:2512.16590, 2025
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Using graph neural networks to predict many-body interactions in amorphous materials
An equivariant GNN trained on DFT energies of solvent-free PGNs reproduces those energies at low cost and drives MC simulations that match experimental structures despite training exclusively on out-of-equilibrium data.