CEMP: an online cheminformatics platform for clean energy materials
- 1State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Research Center of AI for Polymer Science, Fudan University, Shanghai, 200438, China
Abstract
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, https://cleanenergymaterials.cn), 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 ~ 380,000 entries, including ~ 20,000 experimental records, ~ 50,000 quantum-chemical calculations and ~310,000 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. CEMP establishes a unified, source-attributed data and computation infrastructure for clean-energy materials by integrating curated records with automated molecular simulations and machine-learning-based property prediction. Its central contribution is the direct connection of searchable materials data to standardized quantum-chemical and molecular dynamics workflows, enabling users to move from data retrieval to reproducible computation and property screening within a single web-based platform. By bridging materials data, physics-based simulation, and data-driven prediction, CEMP provides an accessible framework for discovery of clean energy materials.
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BibTeX
@article{wang2026cemp,
title = {{CEMP: an online cheminformatics platform for clean energy materials}},
author = {Jifeng Wang and Jiazhe Ju and Ying Wang},
journal = {Journal of Cheminformatics},
year = {2026},
month = sep,
doi = {10.1186/s13321-026-01295-2},
url = {https://doi.org/10.1186/s13321-026-01295-2},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - CEMP: an online cheminformatics platform for clean energy materials
AU - Jifeng Wang
AU - Jiazhe Ju
AU - Ying Wang
PY - 2026
DA - 2026/09/01
JO - Journal of Cheminformatics
DO - 10.1186/s13321-026-01295-2
UR - https://doi.org/10.1186/s13321-026-01295-2
AB - 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, https://cleanenergymaterials.cn), 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 ~ 380,000 entries, including ~ 20,000 experimental records, ~ 50,000 quantum-chemical calculations and ~310,000 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. CEMP establishes a unified, source-attributed data and computation infrastructure for clean-energy materials by integrating curated records with automated molecular simulations and machine-learning-based property prediction. Its central contribution is the direct connection of searchable materials data to standardized quantum-chemical and molecular dynamics workflows, enabling users to move from data retrieval to reproducible computation and property screening within a single web-based platform. By bridging materials data, physics-based simulation, and data-driven prediction, CEMP provides an accessible framework for discovery of clean energy materials.
SN - 1758-2946
ER -