article Quantum Chemistry Machine Learning Potentials Drug Discovery

AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements

Yuxinxin Chen, Yi-Fan Hou, Roman Zubatyuk, Olexandr Isayev, Pavlo O. Dral

2025

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Abstract

The AIQM series methods are successful neural network-based models that target coupled-cluster accuracy while maintaining high robustness and transferability across various tasks by leveraging Δ-learning. However, the previous AIQM1 and AIQM2 models are limited to molecular systems with four elements: H, C, N, and O, which falls short of meeting the common needs for atomistic simulations. Here, we introduce the extension—AIQM3—that covers three additional chemical elements: S, F, Cl, and approaches coupled cluster level at the speed of a semi-empirical method. AIQM3 maintains the accuracy of its predecessor AIQM2, surpasses the commonly used density functional theory (DFT) method in different types of molecular interactions, and its efficiency is competitive with that of machine learning interatomic potentials on commodity CPU hardware. AIQM3 superiority is showcased for reaction simulations and tasks related to drug design, where it delivers accurate torsion profiles for various real-world drug-like molecules. In addition, AIQM3 can be used for infrared (IR) spectra calculations at a low cost. We provide a web service for the AIQM3 calculations on the Aitomistic Hub at aitomistic.xyz, to democratize and facilitate its use with the assistance of AI agents.

Keywords

neural network machine learning density functional

Cite This Paper

@article{Chen2025,
  author = {Chen, Yuxinxin and Hou, Yi-Fan and Zubatyuk, Roman and Isayev, Olexandr and Dral, Pavlo O.},
  title = {AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements},
  year = {2025},
  doi = {10.26434/chemrxiv-2025-g2dbg},
  url = {http://dx.doi.org/10.26434/chemrxiv-2025-g2dbg},
  publisher = {American Chemical Society (ACS)},
  keywords = {neural network, machine learning, density functional},
  researchAreas = {quantum-chemistry, ml-potentials, drug-discovery},
  highlight = {The AIQM series methods are successful neural network-based models that target coupled-cluster accuracy while maintaining high robustness and transferability across various tasks by leveraging Δ-learning.}
}

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Quantum Chemistry Machine Learning Potentials Drug Discovery

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