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Introduces CrystalGRPO, a reinforcement-learning post-training framework for flow-based crystal structure prediction that aligns target recovery and preserves candidate coverage, improving Top-1 and Top-20 performance across MP-20 and MPTS-52 benchmarks.
ED-CSP is a machine learning model that predicts crystal structures from electron diffraction patterns, achieving improved match rates over the PXRD-based PXRDGen and demonstrating the value of multi-view diffraction data.
A collaboration between Ångström AI and AstraZeneca introduces CSP-MACE-Å, a machine learning interatomic potential that aims to replace DFT in crystal structure prediction, achieving comparable accuracy at much lower computational cost.