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PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment
Akshay Subramanian, Elton Pan, Juno Nam +6
Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because ca…
FragmentFlow: Scalable Transition State Generation for Large Molecules
Ron Shprints, Peter Holderrieth, Juno Nam +2
Transition states (TSs) are central to understanding and quantitatively predicting chemical reactivity and reaction mechanisms. Although traditional TS generation methods are compu…
Learning Interatomic Potentials at Multiple Scales
Xiang Fu, Albert Musaelian, Anders Johansson +2
The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a…
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Xiang Fu, Tian Xie, Andrew S. Rosen +2
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modula…
Torsional Diffusion for Molecular Conformer Generation
Bowen Jing, Gabriele Corso, Jeffrey Chang +2
Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-ar…
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley +4
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several d…