Towards practical AI-enhanced computational chemistry

Pavlo O. Dral State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China E-mail: dral@xmu.edu.cn. Website: http://dr-dral.com I will present our methods and software tools enabling practical AI-enhanced computational chemistry simulations and demonstrate their applications. The methods include the general-purpose, artificial intelligence-enhanced quantum mechanical method 1(AIQM1), which approaches the accuracy of the golden-standard, traditional CCSD(T)/CBS approach for many properties. Other methods focus on novel approaches for learning dynamics, such as our AI-quantum dynamics and 4D-spacetime atomistic AI 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 for user-friendly atomistic machine learning simulations, which can be run online using our MLatom@XACS (Xiamen atomistic computing suite) cloud-based service.