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Physical Review. E|January 20, 2018
Inferring low-dimensional microstructure representations using convolutional neural networksNicholas Lubbers, Turab Lookman, Kipton BarrosThe Journal of Chemical Physics|July 2, 2018
Hierarchical modeling of molecular energies using a deep neural networkNicholas Lubbers, Justin S Smith, Kipton BarrosThe Journal of Physical Chemistry. A|April 20, 2023
Machine Learning Models Capture Plasmon Dynamics in Ag NanoparticlesAdela Habib, Nicholas Lubbers, Sergei Tretiak, et al.Physical Review. E|May 20, 2022
Machine learning of consistent thermodynamic models using automatic differentiationDavid Rosenberger, Kipton Barros, Timothy C Germann, et al.The Journal of Chemical Physics|September 16, 2020
Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluidGalen T Craven, Nicholas Lubbers, Kipton Barros, et al.The Journal of Physical Chemistry Letters|May 7, 2020
Ex Machina Determination of Structural Correlation FunctionsGalen T Craven, Nicholas Lubbers, Kipton Barros, et al.Plos Computational Biology|June 8, 2023
Latent Dirichlet Allocation modeling of environmental microbiomesAnastasiia Kim, Sanna Sevanto, Eric R Moore, et al.Proceedings of the National Academy of Sciences of the United States of America|July 1, 2022
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanicsGuoqing Zhou, Nicholas Lubbers, Kipton Barros, et al.Bioinformatics Advances|August 27, 2025
Statistical relationships across epigenomes using large-scale hierarchical clusteringAnastasiia Kim, Nicholas Lubbers, Christina R Steadman, et al.Journal of Chemical Theory and Computation|January 3, 2024
Machine Learning Framework for Modeling Exciton Polaritons in Molecular MaterialsXinyang Li, Nicholas Lubbers, Sergei Tretiak, et al.Pageof 5