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

17 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

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
  • General Chemistry
  • General Chemical Engineering


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