MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
This work addresses the challenge of predicting molecular crystal structures, which is crucial for computational chemistry and materials discovery, representing an incremental advance by extending flow-based methods to periodic molecular crystals.
The authors tackled molecular crystal structure prediction by developing MolCrystalFlow, a flow-based generative model that disentangles intramolecular and intermolecular complexities, achieving state-of-the-art performance on benchmark datasets against existing generative and rule-based methods.
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.