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Journal of Chemical Theory and Computation|May 10, 2023
Semi-Empirical Shadow Molecular Dynamics: A PyTorch ImplementationMaksim Kulichenko, Kipton Barros, Nicholas Lubbers, et al.Scientific Data|May 3, 2020
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for moleculesJustin S Smith, Roman Zubatyuk, Benjamin Nebgen, et al.Scientific Data|October 3, 2022
A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous MediaJavier E Santos, Bernard Chang, Alex Gigliotti, et al.Journal of Chemical Theory and Computation|January 29, 2025
MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction CorrectionsDaniel J Burrill, Chang Liu, Michael G Taylor, et al.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 1, 2021
The Rise of Neural Networks for Materials and Chemical DynamicsMaksim Kulichenko, Justin S Smith, Benjamin Nebgen, et al.The Journal of Chemical Physics|July 9, 2021
Machine learned Hückel theory: Interfacing physics and deep neural networksTetiana Zubatiuk, Benjamin Nebgen, Nicholas Lubbers, et al.Scientific Reports|August 10, 2020
Modeling and scale-bridging using machine learning: nanoconfinement effects in porous mediaNicholas Lubbers, Animesh Agarwal, Yu Chen, et al.Pageof 5