Deep Learning for Molecular Thermodynamics

Hassaan Malik, Muhammad Umar Chaudhry and Michal Jasinski ()
Additional contact information
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

Energies, 2022, vol. 15, issue 24, 1-9

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.

Keywords: deep learning; molecular thermodynamics; thermodynamic properties; artificial intelligence; forecasting; thermodynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/24/9344/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/24/9344/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:24:p:9344-:d:998899

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().