Showing results (11-20 of 42) with videos related to
Sort By:
Pageof 5
The Journal of Chemical Physics|April 28, 2026
Knowledge distillation of noisy force labels for improved coarse-grained force fieldsFeranmi V Olowookere, Sakib Matin, Aleksandra Pachalieva, et al.Journal of Chemical Information and Modeling|April 29, 2025
Including Physics-Informed Atomization Constraints in Neural Networks for Reactive ChemistryShuhao Zhang, Michael Chigaev, Olexandr Isayev, et al.The Journal of Physical Chemistry Letters|May 27, 2026
Enhancing Molecular Dipole Moment Prediction with Multitask Machine LearningWilliam Colglazier, Nicholas Lubbers, Sergei Tretiak, et al.The Journal of Chemical Physics|July 2, 2018
Less is more: Sampling chemical space with active learningJustin S Smith, Ben Nebgen, Nicholas Lubbers, et al.Journal of Chemical Theory and Computation|July 2, 2020
Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorchGuoqing Zhou, Ben Nebgen, Nicholas Lubbers, et al.The Journal of Chemical Physics|May 9, 2023
Lightweight and effective tensor sensitivity for atomistic neural networksMichael Chigaev, Justin S Smith, Steven Anaya, et al.Journal of Chemical Theory and Computation|November 23, 2024
Thermodynamic Transferability in Coarse-Grained Force Fields Using Graph Neural NetworksEmily Shinkle, Aleksandra Pachalieva, Riti Bahl, et al.Journal of Chemical Information and Modeling|August 4, 2021
Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical SearchMichael Tynes, Wenhao Gao, Daniel J Burrill, et al.BMC Bioinformatics|November 22, 2023
Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual informationCullen Roth, Vrinda Venu, Vanessa Job, et al.Nature Computational Science|January 4, 2024
Uncertainty-driven dynamics for active learning of interatomic potentialsMaksim Kulichenko, Kipton Barros, Nicholas Lubbers, et al.Pageof 5