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

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.