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Chemical Science|August 27, 2021
Predicting phosphorescence energies and inferring wavefunction localization with machine learningAndrew E Sifain, Levi Lystrom, Richard A Messerly, 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.Nature Communications|February 24, 2021
Automated discovery of a robust interatomic potential for aluminumJustin S Smith, Benjamin Nebgen, Nithin Mathew, et al.Journal of Chemical Theory and Computation|February 2, 2024
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to ExperimentSakib Matin, Alice E A Allen, Justin Smith, et al.Journal of Chemical Theory and Computation|March 14, 2025
Shadow Molecular Dynamics with a Machine Learned Flexible Charge PotentialCheng-Han Li, Mehmet Cagri Kaymak, Maksim Kulichenko, et al.Journal of the American Chemical Society|July 21, 2026
Coupling High-Throughput Density Functional Theory, Automated Experimentation, and Adaptive Experimental Design To Achieve Selective Rare-Earth Element SeparationsLogan J Augustine, Yufei Wang, Michael G Taylor, et al.Journal of Chemical Theory and Computation|October 27, 2025
GPU-Accelerated Graph-Based Semiempirical Quantum ChemistryMaksim Kulichenko, Robert M Stanton, Cheng-Han Li, 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.Journal of Chemical Theory and Computation|July 14, 2026
Reactive Chemistry at the Unrestricted Coupled Cluster Level: High-Throughput Calculations for Training Machine Learning PotentialsAlice E A Allen, Rui Li, Sakib Matin, 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.Pageof 5