Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression

[2608.14990] Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Condensed Matter > Materials Science arXiv:2608.14990 (cond-mat) [Submitted on 15 Aug 2026] Title: Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression Authors: Yimin Shao , Shengluo Ma , Shenghong Ju , Yijun Shi , Wei Li View a PDF of the paper titled Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression, by Yimin Shao and 4 other authors View PDF Abstract: This work presents a machine learning-symbolic regression (ML-SR) strategy to develop a physically interpretable formula for predicting low pressure CO2 adsorption capacity in hypothetical metal-organic frameworks (hMOFs). Four ML models were trained on a small dataset of 1,000 samples, and five key descriptors-largest cavity diameter, pore limiting diameter, void fraction, gravimetric surface area, and number of hydrogen atoms-were identified through SHAP and feature importance analyses. Symbolic regression was then employed to derive a concise adsorption formula, Q=aA, where a represents an adsorption baseline (mmol/g) and A is a dimensionless adsorption number incorporating four structural descriptors. We interpret A as the ratio between an adsorption binding force and a diffusion driving force, revealing how pore topology and surface chemistry jointly influence adsorption. Validation against a comprehensive dataset of 137,652 hMOFs demonstrates that this formula achieves over 70% prediction accuracy for 62,448 structures, confirming strong applicability within defined structural and operational ranges. Unlike conventional black box ML models, the proposed physics-guided expression enables efficient prediction and provides clearer insight into adsorption mechanisms. Subjects: Materials Science (cond-mat.mtrl-sci) ; Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Chemical Physics (physics.chem-ph) Cite as: arXiv:2608.14990 [cond-mat.mtrl-sci] (or arXiv:2608.14990v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2608.14990 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Shenghong Ju [ view email ] [v1] Sat, 15 Aug 2026 02:34:32 UTC (2,600 KB) Full-text links: Access Paper: View a PDF of the paper titled Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression, by Yimin Shao and 4 other authors View PDF view license Current browse context: cond-mat.mtrl-sci < prev | next > new | recent | 2026-08 Change to browse by: cond-mat cond-mat.mes-hall physics physics.chem-ph References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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