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AI-powered prediction of critical properties and boiling points: a hybrid ensemble learning and QSPR approach
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DOI：10.1186/s13321-025-01062-9.png)
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Abstract
En 中文
In this paper, we propose a robust deep-learning model based on a Quantitative Structure − Property Relationship (QSPR) approach for estimating the critical temperature (TC), critical pressure (PC), acentric factor (ACEN) and normal boiling point (NBP) of any C, H, O, N, S, P, F, Cl, Br, I molecule. The Mordred calculator was used to determine 247 descriptors to characterize the molecules considered in this work. For each evaluated property, multiple neural networks were trained within a bagging framework. The predictions from the final ensemble were successfully tested against a large set of experimental data comprising more than 1700 molecules and compared with those from different recent learning models found in the literature. Comprehensive comparisons and extensive testing highlight the robustness and predictive power of the newly proposed multimodal learning model. The developed prediction tool is available on a website at
https://lrgp-thermoppt.streamlit.app/
. Furthermore, a source code for implementing the trained models in Python is available via github
https://github.com/bounac80/AI-ThermPpt
.
Keywords:
QSPR
deep learning
critical temperature
acentric factor
boiling point
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