Artificial intelligence approach for estimating energy density of liquid metal batteries
Abstract Achieving a high energy density in liquid metal batteries (LMBs) still remains a big challenge. Due to the multitude of affecting parameters within the system, traditional ways may not fully capture the complexity of LMBs. The artificial intelligence approach can be effectively applied to d...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-97287-7 |
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| author | Pouya Zakerabbasi Sina Maghsoudy Alireza Baghban Sajjad Habibzadeh Amin Esmaeili |
| author_facet | Pouya Zakerabbasi Sina Maghsoudy Alireza Baghban Sajjad Habibzadeh Amin Esmaeili |
| author_sort | Pouya Zakerabbasi |
| collection | DOAJ |
| description | Abstract Achieving a high energy density in liquid metal batteries (LMBs) still remains a big challenge. Due to the multitude of affecting parameters within the system, traditional ways may not fully capture the complexity of LMBs. The artificial intelligence approach can be effectively applied to deal with low energy density issues. Herein, we represented the first implementation of the Gaussian Process Regression to predict the LMBs’ energy density to attain the highest accuracy compared to existing models. Four different kernels, namely Exponential, Matern5/2, Rational Quadratic, and Squared Exponential were utilized to achieve the most accurate GPR model. A huge dataset containing 2158 LMB datapoint was gathered from the literature. It contains 41 input parameters, including alloy-related, LMB-related, and creative features. The GPR-Exponential model showed the greatest battery energy density estimate accuracy among the proposed models. The training and testing R2 values were 0.9976 and 0.9975, respectively, indicating the near-perfect accuracy which makes it the most precise model that has been presented so far. According to sensitivity analysis outcomes, it can be claimed that Sb mole fraction, average ionization energy, and average melting temperature with the respective relevancy factors of 0.6672, 0.6550, and 0.6507 could noticeably affect the LMBs’ energy density. Furthermore, the results showed that the LMBs’ energy density is more sensitive to the electrode-dependent and operational parameters rather than the electrolyte situation. |
| format | Article |
| id | doaj-art-5657164fca584d46a06f53e073bd6e46 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-5657164fca584d46a06f53e073bd6e462025-08-20T03:10:12ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-97287-7Artificial intelligence approach for estimating energy density of liquid metal batteriesPouya Zakerabbasi0Sina Maghsoudy1Alireza Baghban2Sajjad Habibzadeh3Amin Esmaeili4Surface reaction and advanced energy materials laboratory, Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic)Surface reaction and advanced energy materials laboratory, Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic)Surface reaction and advanced energy materials laboratory, Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic)Surface reaction and advanced energy materials laboratory, Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic)Department of Chemical Engineering, School of Engineering Technology and Industrial Trades, University of Doha for Science and Technology (UDST)Abstract Achieving a high energy density in liquid metal batteries (LMBs) still remains a big challenge. Due to the multitude of affecting parameters within the system, traditional ways may not fully capture the complexity of LMBs. The artificial intelligence approach can be effectively applied to deal with low energy density issues. Herein, we represented the first implementation of the Gaussian Process Regression to predict the LMBs’ energy density to attain the highest accuracy compared to existing models. Four different kernels, namely Exponential, Matern5/2, Rational Quadratic, and Squared Exponential were utilized to achieve the most accurate GPR model. A huge dataset containing 2158 LMB datapoint was gathered from the literature. It contains 41 input parameters, including alloy-related, LMB-related, and creative features. The GPR-Exponential model showed the greatest battery energy density estimate accuracy among the proposed models. The training and testing R2 values were 0.9976 and 0.9975, respectively, indicating the near-perfect accuracy which makes it the most precise model that has been presented so far. According to sensitivity analysis outcomes, it can be claimed that Sb mole fraction, average ionization energy, and average melting temperature with the respective relevancy factors of 0.6672, 0.6550, and 0.6507 could noticeably affect the LMBs’ energy density. Furthermore, the results showed that the LMBs’ energy density is more sensitive to the electrode-dependent and operational parameters rather than the electrolyte situation.https://doi.org/10.1038/s41598-025-97287-7Liquid metal battery (LMB)Energy storageCathode materialMachine learningGaussian process regression (GPR) |
| spellingShingle | Pouya Zakerabbasi Sina Maghsoudy Alireza Baghban Sajjad Habibzadeh Amin Esmaeili Artificial intelligence approach for estimating energy density of liquid metal batteries Scientific Reports Liquid metal battery (LMB) Energy storage Cathode material Machine learning Gaussian process regression (GPR) |
| title | Artificial intelligence approach for estimating energy density of liquid metal batteries |
| title_full | Artificial intelligence approach for estimating energy density of liquid metal batteries |
| title_fullStr | Artificial intelligence approach for estimating energy density of liquid metal batteries |
| title_full_unstemmed | Artificial intelligence approach for estimating energy density of liquid metal batteries |
| title_short | Artificial intelligence approach for estimating energy density of liquid metal batteries |
| title_sort | artificial intelligence approach for estimating energy density of liquid metal batteries |
| topic | Liquid metal battery (LMB) Energy storage Cathode material Machine learning Gaussian process regression (GPR) |
| url | https://doi.org/10.1038/s41598-025-97287-7 |
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