Problem
Research questions and friction points this paper is trying to address.
molecular crystal structure prediction
CSP
material properties
Packora: Systematic Design for Generative Molecular Crystal Structure Prediction
🤖 AI Summary
本文提出Packora，一种基于流的生成模型，用于预测分子晶体结构，通过从分子图中联合预测原子坐标和晶格来解决CSP问题。
📝 Abstract
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, we evaluate generation and ranking separately, using generation to isolate generator quality and ranking to measure end-to-end performance under a common relaxation and ranking pipeline. We also systematically study architecture, training, conditioning, inference, and scaling, identifying an effective design based on cacheable pairwise reasoning, training objective and numerical solver choices, conditioning dropout, and balanced scaling of pairwise and single representations. Packora outperforms the baselines on both structure generation and ranking benchmarks, achieving the best matched-budget coverage across all six generation benchmarks, as well as higher experimental-form recovery, lower experimental-form ranks, and faster convergence in ranking.
Innovation
Methods, ideas, or system contributions that make the work stand out.
flow-based generative model
molecular crystal structure prediction
multi-component crystals
cacheable pairwise reasoning
conditioning dropout
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