mCGCNN augments crystal graph networks with a magnetic stream and GKA-inspired descriptors to lower MAE for total magnetic moment from 2.54 to 2.02 μB and raise R² from 0.644 to 0.776 on Materials Project DFT data.
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.Chemistry of Materials, 31(9):3564–3572, May 2019
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Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
Loss-guided adaptive scale refinement on NaCl aqueous system reduces overall force MAE from 399.65 to 381.23 by discovering intermediate scales from initial anchors.
Spatial statistics on voxelized structures using FFT correlations and PCA yield low-dimensional convex features that support accurate predictions with as few as 10 training samples.
An autonomous LLM coding agent built the top-performing crystal graph network on the MatBench band-gap benchmark by implementing known methods, outperforming 17 expert models without pretraining.
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Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.