Leveraging Machine Learning for Screening Metal-Organic Frameworks with Selective CO 2 Recognition for Early Thermal Runaway in Lithium-Ion Batteries.
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| Title: | Leveraging Machine Learning for Screening Metal-Organic Frameworks with Selective CO 2 Recognition for Early Thermal Runaway in Lithium-Ion Batteries. |
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| Authors: | Wei, Xian1 (AUTHOR), Li, Xin2 (AUTHOR), Wang, Xiong3 (AUTHOR), Liu, Xiaoyan1,2 (AUTHOR), Zhu, Chen2 (AUTHOR) |
| Source: | Nanomaterials (2079-4991). Feb2026, Vol. 16 Issue 4, p245. 15p. |
| Subjects: | Metal-organic frameworks, Carbon dioxide detectors, Gas absorption & adsorption, Random forest algorithms, Lithium-ion batteries, Machine learning, Detectors |
| Abstract: | The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO2) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO2 recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO2 recognition under multicomponent competitive adsorption conditions involving CO2, C2H4, and O2. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R2 exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q0st(CO2), Q0st(C2H4), WEPA, KH(C2H4), and ETR as key performance drivers. The results indicate that CO2 selectivity is constrained by the binding strength of competing C2H4. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, concentrating in moderate surface areas (965–1975 m2/g), narrow pore windows (PLD ≈ 4–7 Å, LCD ≈ 5.5–9.6 Å), high void fractions above 0.6, and low densities below 1.3 g/cm3. AJOTEY emerges as the optimal candidate with a TSN of 6.43 mol/kg, combining substantial working capacity (4.57 mol/kg) with strong selectivity (25.52). These results will accelerate the discovery of sensing materials and provide a practical pathway for MOF-based CO2 sensor development to enhance lithium-ion battery safety. [ABSTRACT FROM AUTHOR] |
| Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 192039962 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Leveraging Machine Learning for Screening Metal-Organic Frameworks with Selective CO 2 Recognition for Early Thermal Runaway in Lithium-Ion Batteries. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wei%2C+Xian%22">Wei, Xian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xin%22">Li, Xin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiong%22">Wang, Xiong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaoyan%22">Liu, Xiaoyan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Chen%22">Zhu, Chen</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nanomaterials+%282079-4991%29%22">Nanomaterials (2079-4991)</searchLink>. Feb2026, Vol. 16 Issue 4, p245. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Metal-organic+frameworks%22">Metal-organic frameworks</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+dioxide+detectors%22">Carbon dioxide detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Gas+absorption+%26+adsorption%22">Gas absorption & adsorption</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium-ion+batteries%22">Lithium-ion batteries</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO2) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO2 recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO2 recognition under multicomponent competitive adsorption conditions involving CO2, C2H4, and O2. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R2 exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q0st(CO2), Q0st(C2H4), WEPA, KH(C2H4), and ETR as key performance drivers. The results indicate that CO2 selectivity is constrained by the binding strength of competing C2H4. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, concentrating in moderate surface areas (965–1975 m2/g), narrow pore windows (PLD ≈ 4–7 Å, LCD ≈ 5.5–9.6 Å), high void fractions above 0.6, and low densities below 1.3 g/cm3. AJOTEY emerges as the optimal candidate with a TSN of 6.43 mol/kg, combining substantial working capacity (4.57 mol/kg) with strong selectivity (25.52). These results will accelerate the discovery of sensing materials and provide a practical pathway for MOF-based CO2 sensor development to enhance lithium-ion battery safety. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/nano16040245 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 245 Subjects: – SubjectFull: Metal-organic frameworks Type: general – SubjectFull: Carbon dioxide detectors Type: general – SubjectFull: Gas absorption & adsorption Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Lithium-ion batteries Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Detectors Type: general Titles: – TitleFull: Leveraging Machine Learning for Screening Metal-Organic Frameworks with Selective CO 2 Recognition for Early Thermal Runaway in Lithium-Ion Batteries. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wei, Xian – PersonEntity: Name: NameFull: Li, Xin – PersonEntity: Name: NameFull: Wang, Xiong – PersonEntity: Name: NameFull: Liu, Xiaoyan – PersonEntity: Name: NameFull: Zhu, Chen IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20794991 Numbering: – Type: volume Value: 16 – Type: issue Value: 4 Titles: – TitleFull: Nanomaterials (2079-4991) Type: main |
| ResultId | 1 |
