Text-guided flow matching enables sample-efficient crystal structure generation
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Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials design. Text provides a compact way to combine com...
The paper introduces CG-OMatG, an equivariant Riemannian flow-based generative model that predicts molecular crystal structures using a coarse-grained, hierarchical representation. It treats molecules as rigid bodies, performs inter- and intra-molecular message passing, and learns to reconstruct molecule centroids, orientations, and lattice parameters conditioned on chemical species and conformer geometry. The model is trained on OMC25 and CSD datasets, fine-tuned with policy gradient reinforcement learning to favor low-energy structures, and validated against a CSP blind test benchmark using COMPACK packing-similarity analysis.
The study benchmarks 12 deep generative crystal structure prediction models against the template-based TCSP 2.0 on 180 test structures, finding that template retrieval achieves the highest top‑1 success (68.3%). Most generative predictions overlap with template substitutions, and removing entire stoichiometric prototype families from training reduces accuracy by 50‑78%, indicating strong prototype dependence. Only a small subset of predictions remain after such removal, suggesting limited genuine de‑novo capability.
arXiv:2609.26402v1 Announce Type: new Abstract: The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI ha...
TCSP3.0 is an improved template‑based crystal structure prediction method that enforces coefficient‑preserving element mappings, substitutes species simultaneously on parsed structures, and validates each candidate’s composition and cell. It adds periodic‑number retrieval, soft oxidation‑state evidence, radius‑compatibility scoring, structural deduplication, and ORB‑v3 machine‑learning‑potential relaxation and energy ranking, achieving Top‑1 and Top‑20 match rates of 72.2 % and 90.6 % on the T180 benchmark and outperforming previous TCSP2.0 and several deep generative models. The method runs in a median of 28 s per target on two CPU cores, roughly ten times faster than CrystalFlow, with most gains attributed to the correctness fixes and structural deduplication.
Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing can strongly affect...