Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing can strongly affect...
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.
By Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan, Philipp H\"ollmer, Cheng Zeng, Adrian Roitberg, Mingjie Liu, Richard Hennig, Sapna Sarupria, Ellad B. Tadmor, Stefano Martiniani
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.
By Lai Wei, Rongzhi Dong, Ying Feng, Madeline Miklos, Jianjun Hu
AtomSteer is a test‑time inference framework that steers pretrained atomistic generative models to satisfy structural constraints such as crystallographic symmetry, geometry, and stereochemistry. It applies bounded local projections, graph‑rigid stereochemical transformations, and periodic symmetry projections during sampling, accepting updates that improve constraint satisfaction while preserving the rest of the structure. The method is demonstrated on three backbones—FlowMM, ET‑Flow, and Boltz‑2—across tasks including de novo crystal generation, crystal structure prediction, molecular conformer generation, and protein‑ligand complex prediction, yielding improvements in space‑group coverage, stereochemical accuracy, and ligand E/Z error rates.
By Pengfei Cai, Katharina S. C. J\"ager, Utkarsh Utkarsh, Nofit Segal, Bowen Deng, Akshay Subramanian, Rafael Gomez-Bombarelli
arXiv:2606. 03199v1 Announce Type: new Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per molecule.
By Alston Lo, Luka Mucko, Austin H. Cheng, Andy Cai, Alastair J. A. Price, Wojciech Matusik, Al\'an Aspuru-Guzik
uFlowCSP is a MeanFlow-based crystal structure prediction model that learns the average probability‑flow velocity, enabling it to generate complete crystal structures in one to five network evaluations. It achieves inference speeds 5×–58× faster than diffusion and flow‑matching methods while matching or surpassing their performance, with a chemistry‑ and symmetry‑aware Transformer that uses canonical atom ordering and per‑token chemistry embeddings. On the MP‑20 benchmark, uFlowCSP attains comparable or higher accuracy with far fewer evaluations and significantly lower wall‑clock time, demonstrating improved accuracy per network evaluation.
By Sourin Dey, Dipannoy Das Gupta, Lai Wei, Sadman Sadeed Omee, Jianjun Hu