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Nature Communications|December 3, 2021
Artificial intelligence-enhanced quantum chemical method with broad applicabilityPeikun Zheng, Roman Zubatyuk, Wei Wu, et al.Physical Chemistry Chemical Physics : PCCP|June 10, 2010
New insight on structural properties of hydrated nucleic acid bases from ab initio molecular dynamicsAl'ona Furmanchuk, Oleg V Shishkin, Olexandr Isayev, et al.Journal of Chemical Information and Modeling|June 23, 2020
TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network PotentialsXiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, et al.Nature|July 27, 2018
Machine learning for molecular and materials scienceKeith T Butler, Daniel W Davies, Hugh Cartwright, et al.Journal of Chemical Theory and Computation|February 14, 2026
AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed across Seven Main-Group ElementsYuxinxin Chen, Yi-Fan Hou, Roman Zubatyuk, et al.Environmental Pollution (Barking, Essex : 1987)|July 27, 2010
One-electron standard reduction potentials of nitroaromatic and cyclic nitramine explosivesMinori Uchimiya, Leonid Gorb, Olexandr Isayev, 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.Nature Communications|June 6, 2017
Universal fragment descriptors for predicting properties of inorganic crystalsOlexandr Isayev, Corey Oses, Cormac Toher, 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.Annual Review of Physical Chemistry|June 28, 2024
Machine Learning of Reactive PotentialsYinuo Yang, Shuhao Zhang, Kavindri D Ranasinghe, et al.Pageof 10