Accelerating and improving quantum chemistry and dynamics with machine learning
Pavlo O. Dral
State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and
College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
E-mail: [email protected]
State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and
College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
E-mail: [email protected]
Machine learning greatly speeds up quantum mechanical simulations.
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I will present our recent developments in applying machine learning to enhance quantum chemistry and (quantum) molecular dynamics simulations. One of these developments is general-purpose, artificial intelligence-enhanced quantum mechanical method 1 (AIQM1), which approaches the accuracy of golden-standard, traditional CCSD(T)/CBS approach for closed-shell, neutral organic molecules in their ground state at the speed of semiempirical quantum mechanical methods while retaining good accuracy for charged systems and excited states. This method does not need retraining and enables us to perform simulations we have not been able to do with either traditional quantum chemical approaches or with experimental techniques. Another development is our artificial intelligence-based quantum dynamics (AI-QD) approach which does not require iterative trajectory propagation. Finally, I will talk about creating atomistic artificial intelligence models in 4D spacetime as an efficient tool for investigating molecular dynamics. AIQM1 along with many other methods 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.
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Biography
Pavlo O. Dral
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Pavlo O. Dral is a Full Professor at Xiamen University and an Assistant Dean in international admissions matters, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University. His research is focused on accelerating and improving quantum chemistry with artificial intelligence/machine learning. Pavlo Dral is a founder of the MLatom package for atomistic machine learning simulations and a co-founder of the Xiamen Atomistic Computing Suite. In 2021, he was awarded an Outstanding Youth (Overseas) by the National Natural Science Foundation of China. Pavlo Dral did his PhD with Prof. Tim Clark at the University of Erlangen–Nuremberg in 2010–2013, postdoc with Prof. Walter Thiel at the Max Planck Institute for Coal Research in 2013–2019. More information is available on Dral’s group website dr-dral.com.
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