1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
Curvature-Aware Flow Matching for Molecular Structure Generation
Samir Darouich, Juliane Geisler, Jacob W. Toney +4
Generative models produce three-dimensional molecular structures with high geometric accuracy, yet they are trained on distributions of atomic coordinates without explicit access t…
Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity
Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich +1
Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite the known importance of 3D struc…
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
Jacob W. Toney, Samir Darouich, Yiran Wang +3
Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined t…
Beyond the Training Domain: Robust Generative Transition State Models for Unseen Chemistry
Samir Darouich, Jacob W. Toney, Weiliang Luo +3
Transition states (TSs) govern the rates and outcomes of chemical reactions, making their accurate prediction a central challenge in computational chemistry. Although recent machin…
Adaptive Transition State Refinement with Learned Equilibrium Flows
Samir Darouich, Vinh Tong, Tanja Bien +2
Identifying transition states (TSs), the high-energy configurations that molecules pass through during chemical reactions, is essential for understanding and designing chemical pro…