DeepTherm: A Unified Deep Learning Approach to Thermochemistry Prediction for Gas-phase Molecules
Wang, Tairan Brunialti, Sirio Wang, Qi Chen, Xiuying Sarathy, S Mani
Wang, Tairan
Brunialti, Sirio
Wang, Qi
Chen, Xiuying
Sarathy, S Mani
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Natural Language Processing
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Journal article
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English
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Abstract
Accurate thermochemical properties are pivotal in chemical mechanisms for modeling and optimizing complex reactive systems, as they directly impact reaction kinetics and heat release profiles. However, obtaining reliable data for a set of diverse, large, and complex molecules and radicals is challenging due to the limitations of traditional methods. In this work, we introduce DeepTherm, a unified deep learning framework that combines the directed message-passing process for local information and global attention mechanisms for long-range dependencies, effectively addressing the challenges posed by multiple functional groups and large species. DeepTherm integrates graph encoding, transfer learning, pre-defined molecular descriptors, and ensemble learning to achieve high accuracy and robust generalization. A curated dataset of 4,997 species, including closed-shell and open-shell species consisting of C, H, and O atoms, was derived from recent theoretical calculations for model training and testing. The model was rigorously evaluated using both random splits and hierarchical splitting strategies based on molecular scaffolds, functional groups, and molecular complexity. Comparative studies involving 72 model-descriptor combinations demonstrated the superior performance of DeepTherm. To showcase its applications, thermochemical properties for 11,458 species present in the oxidation and pyrolysis reaction mechanisms of 40 hydrocarbons are predicted. The resulting thermochemical data improved the fidelity of detailed gas-phase kinetic simulations compared with experimental measurements. The predictions also showed clear advantages over those based on group-additivity methods. Beyond thermochemical properties, the proposed architecture and unified framework establish a generalizable paradigm for molecular property prediction tasks, offering a scalable foundation for accelerating chemical kinetic modeling and materials discovery.
Citation
T. Wang, S. Brunialti, Q. Wang, X. Chen, S.M. Sarathy, "DeepTherm: A Unified Deep Learning Approach to Thermochemistry Prediction for Gas-phase Molecules," Energy and AI, pp. 100677-100677, 2026, https://doi.org/10.1016/j.egyai.2026.100677.
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Energy and AI
Conference
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46 Information and Computing Sciences, 4613 Theory Of Computation
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Elsevier