MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching

• URL: http://arxiv.org/abs/2602.16020v1
• Date: Tue, 17 Feb 2026 21:22:08 GMT
• Title: MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
• Authors: Cheng Zeng, Harry W. Sullivan, Thomas Egg, Maya M. Martirossyan, Philipp Höllmer, Jirui Jin, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Ellad B. Tadmor, Mingjie Liu,
• Abstract summary: We present MolCrystalFlow, a flow-based generative model for molecular crystal structure prediction.<n>The framework disentangles intramolecular complexity from intermolecular packing by embedding molecules as rigid bodies.<n>We benchmark our model against state-of-the-art generative models for large-size periodic crystals and rule-based structure generation methods.
• Score: 6.02313590078714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids, and metal-organic frameworks, extending such approaches to fully periodic molecular crystals is still elusive. Here, we present MolCrystalFlow, a flow-based generative model for molecular crystal structure prediction. The framework disentangles intramolecular complexity from intermolecular packing by embedding molecules as rigid bodies and jointly learning the lattice matrix, molecular orientations, and centroid positions. Centroids and orientations are represented on their native Riemannian manifolds, allowing geodesic flow construction and graph neural network operations that respects geometric symmetries. We benchmark our model against state-of-the-art generative models for large-size periodic crystals and rule-based structure generation methods on two open-source molecular crystal datasets. We demonstrate an integration of MolCrystalFlow model with universal machine learning potential to accelerate molecular crystal structure prediction, paving the way for data-driven generative discovery of molecular crystals.

Related papers

• OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs [4.5375644408112565]
  We introduce a flow-matching model for predicting organic crystal structures directly from molecular graphs.<n>A bond-aware loss guides the model toward realistic local chemistry by enforcing distributions of bond lengths and connectivity.<n>Experiments show that our method achieves a Match Rate more than 10 times higher than existing baselines.
  arXiv  Detail & Related papers  (2026-02-22T04:01:06Z)
• Molecular Representations in Implicit Functional Space via Hyper-Networks [53.70982267248536]
  We argue that molecular learning can instead be formulated as learning in function space.<n>We instantiate this formulation with MolField, a hyper-network-based framework that learns distributions over molecular fields.<n>Our results show that treating molecules as continuous functions fundamentally changes how molecular representations generalize across tasks.
  arXiv  Detail & Related papers  (2026-01-29T21:13:37Z)
• OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction [63.318434943975255]
  We introduce OXtal, a large-scale 100M parameter all-atom diffusion model that learns the conditional joint distribution over intramolecular conformations and periodic packing.<n>By leveraging a large dataset of 600K experimentally validated crystal structures, OXtal achieves orders-of-improvement over prior ab initio machine learning CSP methods.<n> OXtal attains over 80% packing similarity rate, demonstrating its ability to model both thermodynamic and kinetic regularities of molecular crystallization.
  arXiv  Detail & Related papers  (2025-12-07T20:46:30Z)
• Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation [23.618895235349395]
  We introduce StoL, a diffusion model-based framework that enables rapid and knowledge-free generation of large molecular structures from small-molecule data.<n>StoL assembles molecules in a LEGO-style fashion from scratch, without seeing the target molecules or any structures of comparable size during training.
  arXiv  Detail & Related papers  (2025-11-15T12:20:13Z)
• UniIF: Unified Molecule Inverse Folding [67.60267592514381]
  We propose a unified model UniIF for inverse folding of all molecules. Our proposed method surpasses state-of-the-art methods on all tasks.
  arXiv  Detail & Related papers  (2024-05-29T10:26:16Z)
• Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells [1.515687944002438]
  This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages.
  arXiv  Detail & Related papers  (2023-10-16T02:53:24Z)
• MUDiff: Unified Diffusion for Complete Molecule Generation [104.7021929437504]
  We present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates. We propose a novel graph transformer architecture to denoise the diffusion process. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
  arXiv  Detail & Related papers  (2023-04-28T04:25:57Z)
• An Equivariant Generative Framework for Molecular Graph-Structure Co-Design [54.92529253182004]
  We present MolCode, a machine learning-based generative framework for underlineMolecular graph-structure underlineCo-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Our investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design.
  arXiv  Detail & Related papers  (2023-04-12T13:34:22Z)
• A data-driven interpretation of the stability of molecular crystals [0.0]
  Predicting the stability of crystal structures formed from molecular building blocks is a non-trivial scientific problem. We introduce a structural descriptor tailored to the prediction of the binding energy for a curated dataset of organic crystals. We then interpret this library using a low-dimensional representation of the structure-energy landscape.
  arXiv  Detail & Related papers  (2022-09-21T23:32:53Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models [0.0]
  Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. We describe deep generative models of three dimensional molecular structures using atomic density grids. We are also able to sample diverse sets of molecules based on a given input compound to increase the probability of creating valid, drug-like molecules.
  arXiv  Detail & Related papers  (2020-10-17T01:15:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.