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Machine Learning Applications in Chemical Kinetics and Thermochemistry

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Machine Learning in Molecular Sciences

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 36))

Abstract

Kinetic modeling can predict the performance of a reaction system and aids in understanding detailed reaction chemistry. However, high-fidelity reaction simulations require accurate thermodynamic and kinetic parameters of the involved species, which are not always available, especially for complex reaction networks containing hundreds or thousands of chemicals. This chapter aims to survey recent developments in machine learning in predicting molecular thermochemistry and kinetic properties, with an emphasis on advances in deep learning-based molecular property and reaction kinetics prediction. The pros and cons of commonly used conventional theoretical models are also discussed in this chapter.

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  1. Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan

    Lung-Yi Chen & Yi-Pei Li

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    Hanchao Liu

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Chen, LY., Li, YP. (2023). Machine Learning Applications in Chemical Kinetics and Thermochemistry. In: Qu, C., Liu, H. (eds) Machine Learning in Molecular Sciences. Challenges and Advances in Computational Chemistry and Physics, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-031-37196-7_7

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