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Deep Learning for Molecular Thermodynamics

Author

Listed:
  • Hassaan Malik

    (Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan)

  • Muhammad Umar Chaudhry

    (Department of Computer Science, MNS-University of Agriculture, Multan 60000, Pakistan)

  • Michal Jasinski

    (Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
    Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic)

Abstract

The methods used in chemical engineering are strongly reliant on having a solid grasp of the thermodynamic features of complex systems. It is difficult to define the behavior of ions and molecules in complex systems and to make reliable predictions about the thermodynamic features of complex systems across a wide range. Deep learning (DL), which can provide explanations for intricate interactions that are beyond the scope of traditional mathematical functions, would appear to be an effective solution to this problem. In this brief Perspective, we provide an overview of DL and review several of its possible applications within the realm of chemical engineering. DL approaches to anticipate the molecular thermodynamic characteristics of a broad range of systems based on the data that are already available are also described, with numerous cases serving as illustrations.

Suggested Citation

  • Hassaan Malik & Muhammad Umar Chaudhry & Michal Jasinski, 2022. "Deep Learning for Molecular Thermodynamics," Energies, MDPI, vol. 15(24), pages 1-9, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9344-:d:998899
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