Abstract
Theoretical and computational chemistry aims to develop chemical theory and to apply numerical computation and simulation to reveal the mechanism behind complex chemical phenomena via quantum theory and statistical mechanics. Computation is the third pillar of scientific research together with theory and experiment. Computation enables scientists to test, discover, and build models/theories of the corresponding chemical phenomena. Theoretical and computational chemistry has been advanced to a new era due to the development of high-performance computational facilities and artificial intelligence approaches. The tendency to merge electronic structural theory with quantum chemical dynamics and statistical mechanics is of increasing interest because of the rapid development of on-the-fly dynamic simulations for complex systems plus low-scaling electronic structural theory. Another challenging issue lies in the transition from order to disorder, from thermodynamics to dynamics, and from equilibrium to non-equilibrium. Despite an increasingly rapid emergence of advances in computational power, detailed criteria for databases, effective data sharing strategies, and deep learning workflows have yet to be developed. Here, we outline some challenges and limitations of the current artificial intelligence approaches with an outlook on the potential future directions for chemistry in the big data era.
Article note:
A collection of invited papers on Emerging Technologies and New Directions in Chemistry Research.
Funding source: National Key R&D Program of China
Award Identifier / Grant number: 2020YFA0713601
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 21790340, 22073092
Funding source: Young Elite Scientist Lift-Up
Award Identifier / Grant number: No. YESS20160032
Acknowledgments
This work is a part of “IUPAC organizational structure review” for the “future direction of chemistry survey”. The authors are grateful to Dr. Mark Cesa, Prof. Javier Garcia-Martinez, Dr. Michael Droescher, Dr. Lori Ferrins, and Prof. Ito Chao for their encouragement and fruitful discussions.
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Research funding: This work was funded by National Key Research and Development Program of China (Grant No. 2020YFA0713601; Funder ID: 10.13039/501100012166), the National Natural Science Foundation of China (Grant Nos. 21790340 and 22073092; Funder ID: 10.13039/501100001809), and the Chinese Chemical Society through the “Young Elite Scientist Lift-Up” (Grant No. YESS20160032).
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