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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2605.12067 |
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| _version_ | 1866909036090228736 |
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| author | Florida, Jhon Rogelnor A. Fajardo, Edward Aris D. |
| author_facet | Florida, Jhon Rogelnor A. Fajardo, Edward Aris D. |
| contents | Developing high-performance cathode materials for magnesium-ion batteries (MIBs) remains challenging because Mg$^{2+}$ ions move slowly, and conventional materials exhibit low voltage outputs. In this study, machine learning and first-principles calculations were combined to investigate topological quantum materials (TQMs) as a new class of cathode candidates. A modified crystal graph convolutional neural network (mCGCNN) was used to screen 917 Mg-containing TQMs, identifying a small subset of materials with predicted voltages above 3 V and high volumetric capacities. Among these, Mg$_2$VO$_4$ and Mg$_6$MnO$_8$ were selected for detailed density functional theory (DFT) analysis. Formation energy and convex-hull calculations indicate that Mg$_x$VO$_4$ exhibits a fully stable magnesiation pathway, whereas Mg$_x$MnO$_8$ demonstrates minor metastability at intermediate compositions. The calculated voltage profiles yield average voltages of 3.66 V for Mg$_2$VO$_4$ and 4.06 V for Mg$_6$MnO$_8$, in good agreement with machine learning predictions. Electronic structure analysis, supported by Wannier interpolation, confirms that both materials are semiconducting, with valence bands dominated by O $2p$ states and conduction bands by transition-metal $d$ states, indicating a charge-transfer redox mechanism. Compared to conventional Mg cathodes, these TQMs exhibit higher voltages and competitive capacities, underscoring their potential for next-generation multivalent batteries. This study demonstrates that integrating machine learning with first-principles calculations offers an efficient approach for discovering and understanding novel cathode materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12067 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Discovery of High-Voltage Magnesium-Ion Cathodes using Machine Learning and First-Principles Calculations Florida, Jhon Rogelnor A. Fajardo, Edward Aris D. Materials Science Developing high-performance cathode materials for magnesium-ion batteries (MIBs) remains challenging because Mg$^{2+}$ ions move slowly, and conventional materials exhibit low voltage outputs. In this study, machine learning and first-principles calculations were combined to investigate topological quantum materials (TQMs) as a new class of cathode candidates. A modified crystal graph convolutional neural network (mCGCNN) was used to screen 917 Mg-containing TQMs, identifying a small subset of materials with predicted voltages above 3 V and high volumetric capacities. Among these, Mg$_2$VO$_4$ and Mg$_6$MnO$_8$ were selected for detailed density functional theory (DFT) analysis. Formation energy and convex-hull calculations indicate that Mg$_x$VO$_4$ exhibits a fully stable magnesiation pathway, whereas Mg$_x$MnO$_8$ demonstrates minor metastability at intermediate compositions. The calculated voltage profiles yield average voltages of 3.66 V for Mg$_2$VO$_4$ and 4.06 V for Mg$_6$MnO$_8$, in good agreement with machine learning predictions. Electronic structure analysis, supported by Wannier interpolation, confirms that both materials are semiconducting, with valence bands dominated by O $2p$ states and conduction bands by transition-metal $d$ states, indicating a charge-transfer redox mechanism. Compared to conventional Mg cathodes, these TQMs exhibit higher voltages and competitive capacities, underscoring their potential for next-generation multivalent batteries. This study demonstrates that integrating machine learning with first-principles calculations offers an efficient approach for discovering and understanding novel cathode materials. |
| title | Discovery of High-Voltage Magnesium-Ion Cathodes using Machine Learning and First-Principles Calculations |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.12067 |