Deep Learning for Molecular Thermodynamics
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/24/9344 |
| _version_ | 1797459759143583744 |
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| author | Hassaan Malik Muhammad Umar Chaudhry Michal Jasinski |
| author_facet | Hassaan Malik Muhammad Umar Chaudhry Michal Jasinski |
| author_sort | Hassaan Malik |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:56:53Z |
| format | Article |
| id | doaj.art-99e4c5acbbae4cfe9c11b70784338c68 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:56:53Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-99e4c5acbbae4cfe9c11b70784338c682023-11-24T14:35:09ZengMDPI AGEnergies1996-10732022-12-011524934410.3390/en15249344Deep Learning for Molecular ThermodynamicsHassaan Malik0Muhammad Umar Chaudhry1Michal Jasinski2Department of Computer Science, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, MNS-University of Agriculture, Multan 60000, PakistanDepartment of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandThe 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.https://www.mdpi.com/1996-1073/15/24/9344deep learningmolecular thermodynamicsthermodynamic propertiesartificial intelligenceforecastingthermodynamics |
| spellingShingle | Hassaan Malik Muhammad Umar Chaudhry Michal Jasinski Deep Learning for Molecular Thermodynamics Energies deep learning molecular thermodynamics thermodynamic properties artificial intelligence forecasting thermodynamics |
| title | Deep Learning for Molecular Thermodynamics |
| title_full | Deep Learning for Molecular Thermodynamics |
| title_fullStr | Deep Learning for Molecular Thermodynamics |
| title_full_unstemmed | Deep Learning for Molecular Thermodynamics |
| title_short | Deep Learning for Molecular Thermodynamics |
| title_sort | deep learning for molecular thermodynamics |
| topic | deep learning molecular thermodynamics thermodynamic properties artificial intelligence forecasting thermodynamics |
| url | https://www.mdpi.com/1996-1073/15/24/9344 |
| work_keys_str_mv | AT hassaanmalik deeplearningformolecularthermodynamics AT muhammadumarchaudhry deeplearningformolecularthermodynamics AT michaljasinski deeplearningformolecularthermodynamics |