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Packora: Systematic Design for Generative Molecular Crystal Structure Prediction
Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing can strongly affect material properties. We present Packora, a flow-based generative model for molecular CSP that jointly predicts atomic coordinates and the lattice from molecular graphs. Packora supports multi-component and organometallic crystals and can condition on any subset of molecular conformers, stereochemical labels, and space-group information within a single model. Inspired by the CCDC CSP blind test, w
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T11:01:18.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.