AI-enhanced chemical physics simulations

I will present our methods and software tools enabling practical AI-enhanced chemical physics simulations and demonstrate their applications. The methods include the general-purpose, artificial intelligence-enhanced quantum mechanical method 1 (AIQM1), [2] which for many properties approaches the accuracy of golden-standard, traditional CCSD(T)/CBS approach while being orders of magnitude faster than DFT. Other methods focus on novel approaches for learning dynamics such as our AI-quantum dynamics [3] and 4D-spacetime atomistic AI [4] approaches which predict dynamics properties such as nuclear coordinates as the function of time and do not require iterative trajectory propagation as in classical MD. AIQM1 and AI-QD along with many other methods such as a host of ML interatomic potentials are implemented in our MLatom program package and Python library for user-friendly atomistic machine learning simulations which can be run online using our MLatom@XACS cloud-based service. [5]