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Nature Communications|July 3, 2019
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learningJustin S Smith, Benjamin T Nebgen, Roman Zubatyuk, et al.Journal of Chemical Theory and Computation|August 2, 2018
Transferable Dynamic Molecular Charge Assignment Using Deep Neural NetworksBenjamin Nebgen, Nicholas Lubbers, Justin S Smith, et al.The Journal of Chemical Physics|September 15, 2023
Synergy of semiempirical models and machine learning in computational chemistryNikita Fedik, Benjamin Nebgen, Nicholas Lubbers, et al.The Journal of Physical Chemistry Letters|July 25, 2018
Discovering a Transferable Charge Assignment Model Using Machine LearningAndrew E Sifain, Nicholas Lubbers, Benjamin T Nebgen, et al.Journal of Molecular Modeling|May 31, 2012
Validation of a novel secretion modification region (SMR) of HIV-1 Nef using cohort sequence analysis and molecular modelingPatrick E Campbell, Olexandr Isayev, Syed A Ali, et al.Journal of Chemical Theory and Computation|February 3, 2023
Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous MaterialsKaihang Shi, Zhao Li, Dylan M Anstine, et al.RSC Advances|May 11, 2022
Adsorption of nitrogen-containing compounds on hydroxylated α-quartz surfacesOksana Tsendra, A Daniel Boese, Olexandr Isayev, et al.Communications Chemistry|January 25, 2023
Generative and reinforcement learning approaches for the automated de novo design of bioactive compoundsMaria Korshunova, Niles Huang, Stephen Capuzzi, et al.Nature Reviews. Chemistry|April 28, 2023
Extending machine learning beyond interatomic potentials for predicting molecular propertiesNikita Fedik, Roman Zubatyuk, Maksim Kulichenko, et al.Nature Chemistry|March 7, 2024
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialShuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, et al.Pageof 10