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Journal of the American Chemical Society|April 13, 2023
Generative Models as an Emerging Paradigm in the Chemical SciencesDylan M Anstine, Olexandr IsayevThe Journal of Physical Chemistry. A|February 21, 2023
Machine Learning Interatomic Potentials and Long-Range PhysicsDylan M Anstine, Olexandr IsayevChemical Science|May 9, 2025
AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needsDylan M Anstine, Roman Zubatyuk, Olexandr IsayevChemical Science|November 30, 2023
Δ<sup>2</sup> machine learning for reaction property predictionQiyuan Zhao, Dylan M Anstine, Olexandr Isayev, et al.Angewandte Chemie (International Ed. in English)|December 16, 2025
AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell ChemistryBhupalee Kalita, Roman Zubatyuk, Dylan M Anstine, et al.Crystal Growth & Design|November 10, 2025
Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network PotentialsKamal Singh Nayal, Dana O'Connor, Roman Zubatyuk, et al.Angewandte Chemie (International Ed. in English)|July 13, 2025
Design of Tough 3D Printable Elastomers with Human-in-the-Loop Reinforcement LearningJohann L Rapp, Dylan M Anstine, Filipp Gusev, et al.Chemical Science|March 21, 2022
Prediction of protein p<i>K</i> <sub>a</sub> with representation learningHatice Gokcan, Olexandr IsayevAccounts of Chemical Research|March 15, 2021
Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial IntelligenceTetiana Zubatiuk, Olexandr IsayevChemical Science|October 9, 2025
High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learningPeikun Zheng, Olexandr IsayevPageof 10