Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach
Abstract The Gibbs free energy of an inorganic material represents its maximum reversible work potential under constant temperature and pressure. Its calculation is crucial for understanding material stability, phase transitions, and chemical reactions, thus guiding optimization for diverse applicat...
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| Language: | English |
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BMC
2025-06-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-01033-0 |
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| author | Raheel Hammad Sownyak Mondal |
| author_facet | Raheel Hammad Sownyak Mondal |
| author_sort | Raheel Hammad |
| collection | DOAJ |
| description | Abstract The Gibbs free energy of an inorganic material represents its maximum reversible work potential under constant temperature and pressure. Its calculation is crucial for understanding material stability, phase transitions, and chemical reactions, thus guiding optimization for diverse applications like catalysis and energy storage. In this study, we have developed a Physics-Informed Neural Network model that leverages the Gibbs free energy equation. The overall loss function is adjusted to allow the model to simultaneously predict all three thermodynamic quantities, including Gibbs free energy, total energy, and entropy, thus transforming it into a multi-output model. In recent literature, there is a growing emphasis on evaluating machine learning models under challenging conditions, such as small datasets and out-of-distribution predictions. Reflecting this trend, we have rigorously benchmarked our model across these scenarios, demonstrating its robustness and adaptability. It turns out that our model demonstrates a 43% improvement for normal scenario and even more in out-of-distribution regime compared to the next-best model. Scientific Contribution This study introduces the application of a Physics-Informed Neural Network to simultaneously compute multiple thermodynamic properties, including Gibbs free energy, total energy, and entropy. By integrating the Gibbs free energy equation into the loss function, the model achieves superior accuracy in low data regimes and enhances robustness in the out-of-distribution scenarios. |
| format | Article |
| id | doaj-art-963d55c89e514728a4f56c1a799b70a4 |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-963d55c89e514728a4f56c1a799b70a42025-08-20T04:03:07ZengBMCJournal of Cheminformatics1758-29462025-06-011711810.1186/s13321-025-01033-0Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approachRaheel Hammad0Sownyak Mondal1Tata Institute of Fundamental Research HyderabadTata Institute of Fundamental Research HyderabadAbstract The Gibbs free energy of an inorganic material represents its maximum reversible work potential under constant temperature and pressure. Its calculation is crucial for understanding material stability, phase transitions, and chemical reactions, thus guiding optimization for diverse applications like catalysis and energy storage. In this study, we have developed a Physics-Informed Neural Network model that leverages the Gibbs free energy equation. The overall loss function is adjusted to allow the model to simultaneously predict all three thermodynamic quantities, including Gibbs free energy, total energy, and entropy, thus transforming it into a multi-output model. In recent literature, there is a growing emphasis on evaluating machine learning models under challenging conditions, such as small datasets and out-of-distribution predictions. Reflecting this trend, we have rigorously benchmarked our model across these scenarios, demonstrating its robustness and adaptability. It turns out that our model demonstrates a 43% improvement for normal scenario and even more in out-of-distribution regime compared to the next-best model. Scientific Contribution This study introduces the application of a Physics-Informed Neural Network to simultaneously compute multiple thermodynamic properties, including Gibbs free energy, total energy, and entropy. By integrating the Gibbs free energy equation into the loss function, the model achieves superior accuracy in low data regimes and enhances robustness in the out-of-distribution scenarios.https://doi.org/10.1186/s13321-025-01033-0American Chemical Society |
| spellingShingle | Raheel Hammad Sownyak Mondal Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach Journal of Cheminformatics American Chemical Society |
| title | Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach |
| title_full | Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach |
| title_fullStr | Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach |
| title_full_unstemmed | Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach |
| title_short | Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach |
| title_sort | advancements in thermochemical predictions a multi output thermodynamics informed neural network approach |
| topic | American Chemical Society |
| url | https://doi.org/10.1186/s13321-025-01033-0 |
| work_keys_str_mv | AT raheelhammad advancementsinthermochemicalpredictionsamultioutputthermodynamicsinformedneuralnetworkapproach AT sownyakmondal advancementsinthermochemicalpredictionsamultioutputthermodynamicsinformedneuralnetworkapproach |