1 citations · 1 across the 6 of their papers we have counts for
8 papers
Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models
Tanja Bien, Samir Darouich, Lluís Pastor-Pérez +3
Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated samples. However, improving con…
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…
ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation
Loay Mualem, Vinh Tong, Samir Darouich +1
Distilling conditional diffusion models aims to transfer the behavior of a large teacher to a smaller student while preserving alignment across conditioning inputs. Unlike recognit…
SymDrift: One-Shot Generative Modeling under Symmetries
Samir Darouich, Vinh Tong, Lluís Pastor-Pérez +3
Generative modeling of physical systems, such as molecules, requires learning distributions that are invariant under global symmetries, such as rotations in three-dimensional space…
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…