The AIQM series of 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 fall short of meeting the common needs for atomistic simulations. Here, we introduce the extension─AIQM3─that covers three additional chemical elements: S, F, and Cl, and approaches coupled-cluster level at the speed of a semiempirical 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's 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. Remarkably, without training on radicals and charged species, AIQM3 can deliver near-DFT accuracy on the binding energy of ion pairs and the radical stabilization energies. In addition, AIQM3 can be used for infrared (IR) spectra calculations at low cost. We provide a web service for the AIQM3 calculations on the Aitomistic Hub at aitomistic.xyz and Aitomistic Lab@XMU (https://atom.xmu.edu.cn, free for academic users), to democratize and facilitate its use with the assistance of AI agents.
Chen et al. (2026) studied this question.