CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916829414293504 |
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| author | Wang, Jifeng Ju, Jiazhe Wang, Ying |
| author_facet | Wang, Jifeng Ju, Jiazhe Wang, Ying |
| contents | The development of materials science is undergoing a shift from empirical approaches to data-driven and algorithm-oriented research paradigm. The state-of-the-art platforms are confined to inorganic crystals, with limited chemical space, sparse experimental data and a lack of integrated online computation for rapid validation. Here, we introduce the Clean Energy Materials Platform (CEMP), an open-access platform that integrates high-throughput computing workflows, multi-scale machine learning (ML) models and a comprehensive materials database tailored for clean energy applications. A key feature of CEMP is the online calculation module, which enables fully automatic quantum and molecular dynamics simulations via structured table uploads. CEMP harmonizes heterogeneous data from experimental measurements, theoretical calculation and AI-based predictions for four material classes, including small molecules, polymers, ionic liquids, and crystals. The platform hosts ~ 376,000 entries, including ~6,000 experimental records, ~50,000 quantum-chemical calculations and ~320,000 AI-predicted properties. The database covers 12 critical properties and the corresponding ML models demonstrate robust predictive power with R2 ranging from 0.64 to 0.94, thus ensures rapid material screening, structure-property relationship analysis and multi-objective optimization for clean energy applications. CEMP aims to establish a digital ecosystem for clean energy materials, enabling a closed-loop workflow from data acquisition to material discovery and real-time online validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials Wang, Jifeng Ju, Jiazhe Wang, Ying Materials Science Soft Condensed Matter Chemical Physics The development of materials science is undergoing a shift from empirical approaches to data-driven and algorithm-oriented research paradigm. The state-of-the-art platforms are confined to inorganic crystals, with limited chemical space, sparse experimental data and a lack of integrated online computation for rapid validation. Here, we introduce the Clean Energy Materials Platform (CEMP), an open-access platform that integrates high-throughput computing workflows, multi-scale machine learning (ML) models and a comprehensive materials database tailored for clean energy applications. A key feature of CEMP is the online calculation module, which enables fully automatic quantum and molecular dynamics simulations via structured table uploads. CEMP harmonizes heterogeneous data from experimental measurements, theoretical calculation and AI-based predictions for four material classes, including small molecules, polymers, ionic liquids, and crystals. The platform hosts ~ 376,000 entries, including ~6,000 experimental records, ~50,000 quantum-chemical calculations and ~320,000 AI-predicted properties. The database covers 12 critical properties and the corresponding ML models demonstrate robust predictive power with R2 ranging from 0.64 to 0.94, thus ensures rapid material screening, structure-property relationship analysis and multi-objective optimization for clean energy applications. CEMP aims to establish a digital ecosystem for clean energy materials, enabling a closed-loop workflow from data acquisition to material discovery and real-time online validation. |
| title | CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials |
| topic | Materials Science Soft Condensed Matter Chemical Physics |
| url | https://arxiv.org/abs/2507.04423 |