Machine learning for molecular thermodynamics

Jiaqi Ding, Nan Xu, Manh Tien Nguyen, Qi Qiao, Yao Shi, Yi He, Qing Shao

Research output: Contribution to journalReview articlepeer-review

8 Scopus citations


Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes. It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems. Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions. This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples. The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data. The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials. The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations. The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering. We will also discuss the perspective of the broad applications of machine learning in chemical engineering.

Original languageEnglish
Pages (from-to)227-239
Number of pages13
JournalChinese Journal of Chemical Engineering
StatePublished - Mar 2021

Bibliographical note

Funding Information:
Jiaqi Ding, Nan Xu, Dr. Yao Shi and Dr. Yi He acknowledge financial supports from the National Natural Science Foundation of China (grant number 21676245 and 51933009), the National Key Research and Development Program of China (grant number 2017YFB0702502), and the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (grant number 2019R01006). Manh Tien Nguyen, Dr. Qi Qiao and Dr. Qing Shao thank the financial support provided by the Startup Funds of the University of Kentucky.

Publisher Copyright:
© 2021 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd


  • Force field
  • Machine learning
  • Molecular engineering
  • Molecular simulation
  • Thermodynamic properties

ASJC Scopus subject areas

  • Environmental Engineering
  • Biochemistry
  • Chemistry (all)
  • Chemical Engineering (all)


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