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| Domain | Dataset or Paper Name | Access URL | DOI | Rename in Our Dataset |
|---|---|---|---|---|
| photocatalysis | Machine learning aided design of perovskite oxide materials for photocatalytic water splitting | https://www.sciencedirect.com/science/article/pii/S2095495621000644#s0090 | 10.1016/j.jechem.2021.01.035 | table1 table2 table3 |
| photocatalysis | Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production | https://www.sciencedirect.com/science/article/pii/S0926337318309470#sec0130 | 10.1016/j.apcatb.2018.09.104 | table4 table5 |
| photocatalysis | An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique | https://www.nature.com/articles/s41598-022-10563-8#Sec10 | 10.1038/s41598-022-10563-8 | table6 |
| photocatalysis | Analysis of photocatalytic CO2 reduction over MOFs using machine learning | https://pubs.rsc.org/en/content/articlelanding/2024/ta/d3ta07001h | 10.1039/D3TA07001H | table7 |
| photocatalysis | Data-driven for accelerated design strategy of photocatalytic degradation activity prediction of doped TiO2 photocatalyst | https://www.sciencedirect.com/science/article/pii/S2214714422005700#s0055 | 10.1016/j.jwpe.2022.103126 | table8 |
| photocatalysis | A generalized predictive model for TiO2–Catalyzed photo-degradation rate constants of water contaminants through ANN | https://www.sciencedirect.com/science/article/pii/S0013935120305909 | 10.1016/j.envres.2020.109697 | table9 |
| photocatalysis | Statistical information review of CO2 photocatalytic reduction via bismuth-based photocatalysts using ANN | https://www.sciencedirect.com/science/article/pii/S1110016824008640?via%3Dihub | 10.1016/j.aej.2024.07.120 | table10 |
| photocatalysis | Accelerated Design for Perovskite-Oxide-Based Photocatalysts Using Machine Learning Techniques | https://www.mdpi.com/1996-1944/17/12/3026 | 10.3390/ma17123026 | table11 |
| electrocatalysis | Building Blocks for High Performance in Electrocatalytic CO2 Reduction | https://acs.figshare.com/articles/dataset/Building_Blocks_for_High_Performance_in_Electrocatalytic_CO_sub_2_sub_Reduction/5293804 | 10.1021/acs.jpclett.7b01380 | table12 |
| electrocatalysis | Unlocking New Insights for Electrocatalyst Design: A Unique Data Science Workflow | https://github.com/ruiding-uchicago/InCrEDible-MaT-GO | 10.1021/acscatal.3c01914 | table13 |
| electrocatalysis | Perovskite-based electrocatalyst discovery and design using word embeddings from retrained SciBERT | https://github.com/arunm917/Perovskite-based-electrocatalyst-design-and-discovery | - | table14 |
| electrocatalysis | Exploring the Composition Space of High-Entropy Alloy Nanoparticles with Bayesian Optimization | https://github.com/vamints/Scripts_BayesOpt_PtRuPdRhAu_paper | 10.1021/acscatal.2c02563 | table15 |
| electrocatalysis | High Throughput Discovery of Complex Metal Oxide Electrocatalysts for Oxygen Reduction Reaction | https://data.caltech.edu/records/1km87-52j70 | 10.1007/s12678-021-00694-3 | table16 |
| photoelectrocatalysis | High-thoughput OCM data | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/21010bbe-0a5c-4d12-a5fa-84eea540e4be/ | 10.1021/acscatal.9b04293 | table17 |
| photoelectrocatalysis | CatApp Data | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/20de069b-53cf-4310-9090-1738f53231e2/ | 10.1002/anie.201107947 | table18 |
| photoelectrocatalysis | Oxidative Coupling of Methane | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/9436f770-a7e2-4e87-989b-c5a9ce2312bf/ | 10.1002/cctc.202001032 | table19 |
| photoelectrocatalysis | ChemCatChem | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/224dd7ad-7677-4161-b744-a0c796bf5347/ | 10.1002/cctc.201100186 | table20 |
| photoelectrocatalysis | HTP OCM data obtained with catalysts designed on the basis of heuristics derived from random catalyst data | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/92200ba4-7644-44ca-9801-ed3cc52fc32f/ | 10.1002/cctc.202100460 | table21 |
| photoelectrocatalysis | Perovskite Data | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/f1b42c58-a423-4ec2-8bcf-e66c6470ff7d/ | 10.1039/C2EE22341D | table22 |
| photoelectrocatalysis | Random catalyst OCM data by HTE | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/f7e30001-e440-4c1a-be64-ea866b2f77cb/ | 10.1021/acscatal.0c04629 | table23 |
| photoelectrocatalysis | Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/f2a6d4f2-91be-48ba-bf13-ffebbd90f6ee/ | 10.1021/jacs.2c06143 | table24 |
| photoelectrocatalysis | Multi-component La2O3- based catalysts in OCM | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/d6347fc1-e4d7-412e-aed5-a8ffa415a703/ | 10.1039/D1CY02206G | table25 |
| photoelectrocatalysis | Catalyst Modification in OCM via Manganese Promoter | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/32dbec2c-c3d5-43ec-962a-90dba719bb44/ | 10.1021/acs.iecr.1c05079 | table26 |
| photoelectrocatalysis | Leveraging Machine Learning Engineering to Uncover Insights in Heterogeneous Catalyst Design for OCM | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/d84c1e22-ceb9-488a-8d45-4c7cf1c603b5/ | 10.1039/D3CY00596H | table27 |
| photoelectrocatalysis | Oxidative of Coupling Literature and Highthroughput Data | https://cads.eng.hokudai.ac.jp/datamanagement/datasources/adb27910-d0e5-4a22-9415-580bf597035a/ | 10.1021/acscatal.0c04629, 10.1002/cctc.201100186 | table28 |
| photoelectrocatalysis | Catalytic Material Database | http://cmd.us.edu.pl/catalog/ | - | table29 |
| photoelectrocatalysis | Catalyst Hub | http://www.catalysthub.net/ | - | table31 |
| magnetic material | Magnetic Database | https://doi.org/10.15131/shef.data.24008055.v1 | 10.1063/9.0000657 | table32 |
| magnetic material | Materials database of Curie and Néel magnetic phase transition temperatures | https://doi.org/10.6084/m9.figshare.5702740.v1 | 10.1038/sdata.2018.111 | table33 |
| magnetic material | Data-driven design of molecular nanomagnets | https://go.uv.es/rosaleny/SIMDAVIS | 10.1038/s41467-022-35336-9 | table34 |
| perovskite | Predicting the thermodynamic stability of perovskite oxides using machine learning models | https://doi.org/10.1016/j.dib.2018.05.007 | - | table35 table36 table37 |
| others | Crystallography Open Database(COD) | http://www.crystallography.net/cod/ | - | table38 |
| others | Alloy synthesis and processing by semi-supervised text mining | https://www.nature.com/articles/s41524-023-01138-w | 10.1038/s41524-023-01138-w | table39 |
| others | A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction | https://github.com/olivettigroup/table_extractor | 10.1021/acscentsci.9b00193 | table40 |
| others | ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters | https://github.com/eltonpan/zeosyn_dataset | 10.1021/acscentsci.3c01615 | table41 |
| others | Unveiling the Potential of AI for Nanomaterial Morphology Prediction | https://github.com/acid-design-lab/Nanomaterial_Morphology_Prediction | 预印本：10.48550/arXiv.2406.02591 | table42 |
| others | AFLOW-2 CFID dataset 400k | https://doi.org/10.1016/j.commatsci.2012.02.005 | 10.1016/j.commatsci.2012.02.005 | table43 |
| others | Alexandria_DB PBE 3D all 5 million | https://alexandria.icams.rub.de/ | - | table44 |
| others | arXiv dataset 1.8 million | https://www.kaggle.com/Cornell-University/arxiv | - | table45 |
| others | CCCBDB dataset 1333 | https://cccbdb.nist.gov/ | - | table46 |
| others | 3D dataset 55k | https://www.nature.com/articles/s41524-020-00440-1 | 10.1038/s41524-020-00440-1 | table47 |
| others | 2D dataset 1.1k | https://www.nature.com/articles/s41524-020-00440-1 | 10.1038/s41524-020-00440-1 | table48 |
| others | halide perovskite dataset229 | https://doi.org/10.1039/D1EE02971A | 10.1039/D1EE02971A | table49 |
| others | hMOF dataset 137k | https://doi.org/10.1021/acs.jpcc.6b08729 | 10.1021/acs.jpcc.6b08729 | table50 |
| others | HOPV15 dataset 4.5k | https://www.nature.com/articles/sdata201686 | 10.1038/sdata.2016.86 | table51 |
| others | Surface property dataset 607 | https://doi.org/10.1039/D4DD00031E | 10.1039/D4DD00031E | table52 |
| others | JARVIS-FF 2k | https://www.nature.com/articles/s41524-020-00440-1 | 10.1038/s41524-020-00440-1 | table53 |
| others | MEGNET-3D CFID dataset 69k | - | - | table54 |
| others | Materials Project-3D CFID dataset 127k | https://next-gen.materialsproject.org/ | 10.1063/1.4812323 | table55 |
| others | Materials Project-3D CFID dataset 84k | - | - | table56 |
| others | OQMD-3D dataset 800k | https://www.oqmd.org/download/ | 10.1038/npjcompumats.2015.10 | table57 |
| others | Polymer genome 1k | https://datadryad.org/dataset/doi:10.5061/dryad.5ht3n | 10.1038/sdata.2016.12 | table58 |
| others | QETB dataset 860k | https://arxiv.org/abs/2112.11585 | 预印本：10.48550/arXiv.2112.11585 | table59 |
| others | QM9 dataset 130k, from DGL | https://www.nature.com/articles/sdata201422 | 10.1038/sdata.2014.22 | table60 |
| others | QM9 standardized dataset 130k | - | - | table61 |
| others | QMOF dataset 20k | https://www.cell.com/matter/fulltext/S2590-2385(21)00070-9 | 10.1016/j.matt.2021.02.015 | table62 |
| others | SNUMAT Hybrid functional dataset 10k | https://www.nature.com/articles/s41597-020-00723-8 | 10.1038/s41597-020-00723-8 | table63 |
| others | SSUB dataset 1726 | https://github.com/wolverton-research-group/qmpy | - | table64 |
| others | chem dataset 16414 | https://www.nature.com/articles/s41524-018-0085-8 | 10.1038/s41524-018-0085-8 | table65 |
| others | dataset 607 | https://doi.org/10.1039/D4DD00031E | 10.1039/D4DD00031E | table66 |
| others | 2DMatPedia dataset 6k | http://www.2dmatpedia.org/ | 10.1038/s41597-019-0097-3 | table67 |
| others | vacancy dataset 464 | https://doi.org/10.1063/5.0135382 | 10.1063/5.0135382 | table68 |