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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.14234 |
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| _version_ | 1866909817151422464 |
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| author | Therrien, Félix Haibeh, Jamal Abou Sharma, Divya Hendley, Rhiannon Mungai, Leah Wairimu Sun, Sun Tchagang, Alain Su, Jiang Huberman, Samuel Bengio, Yoshua Guo, Hongyu Hernández-García, Alex Shin, Homin |
| author_facet | Therrien, Félix Haibeh, Jamal Abou Sharma, Divya Hendley, Rhiannon Mungai, Leah Wairimu Sun, Sun Tchagang, Alain Su, Jiang Huberman, Samuel Bengio, Yoshua Guo, Hongyu Hernández-García, Alex Shin, Homin |
| contents | Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a database of $\sim$600 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities gathered from literature and curated by domain experts. Each material is described by their measured composition, space group and lattice parameters. A full-crystal description in the form of a crystallographic information file (CIF) is provided for $\sim$320 structures for which atomic positions were available. We discuss various statistics and features of the dataset and provide training and testing splits carefully designed to avoid data leakage. Finally, we benchmark seven existing ML models on the task of predicting ionic conductivity and discuss their performance. The goal of this work is to facilitate the use of machine learning for solid-state electrolyte materials discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes Therrien, Félix Haibeh, Jamal Abou Sharma, Divya Hendley, Rhiannon Mungai, Leah Wairimu Sun, Sun Tchagang, Alain Su, Jiang Huberman, Samuel Bengio, Yoshua Guo, Hongyu Hernández-García, Alex Shin, Homin Materials Science Machine Learning Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a database of $\sim$600 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities gathered from literature and curated by domain experts. Each material is described by their measured composition, space group and lattice parameters. A full-crystal description in the form of a crystallographic information file (CIF) is provided for $\sim$320 structures for which atomic positions were available. We discuss various statistics and features of the dataset and provide training and testing splits carefully designed to avoid data leakage. Finally, we benchmark seven existing ML models on the task of predicting ionic conductivity and discuss their performance. The goal of this work is to facilitate the use of machine learning for solid-state electrolyte materials discovery. |
| title | OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2502.14234 |