Universal and Updatable Artificial Intelligence-Enhanced Quantum Chemical Foundational Models

Quantum chemical methods developed since 1927 are instrumental in chemical simulations but human expertise has been still essential in choosing a suitable method. Here we introduce a paradigm shift to universal and updatable artificial intelligence-enhanced quantum mechanical (UAIQM) foundational models with an online platform auto-selecting the models with the best accuracy for the given system, available time, and moderate computational resources (see https://xacs.xmu.edu.cn/docs/mlatom/tutorial_uaiqm.html for instructions). The platform hosts a growing library of state-of-the-art UAIQM models with calibrated uncertainties and provides a mechanism for improving the foundational models continuously with more usage. We demonstrate how the UAIQM platform can be used for massive accurate simulations within hours on a commodity hardware which would take days or weeks on high-performance computing centers with less accurate workhorse quantum chemical methods. We also show that UAIQM sets a new standard for infrared spectra, reaction barriers, and energetics whose accurate predictions can have far-reaching consequences in molecular simulations.