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Cited by in F6Publishing
For: Pan Z, Zhou Y, Zhang L. Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water. ACS Appl Mater Interfaces 2022. [PMID: 35147024 DOI: 10.1021/acsami.2c00568] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Hu W, Zhang L. High-Throughput Calculation and Machine Learning of Two-Dimensional Halide Perovskite Materials: Formation Energy and Band Gap. Materials Today Communications 2023. [DOI: 10.1016/j.mtcomm.2023.105841] [Reference Citation Analysis]
2 Niu X, Dang Y, Sun Y, Hu W. Judicious training pattern for superior molecular reorganization energy prediction model. Journal of Energy Chemistry 2023. [DOI: 10.1016/j.jechem.2023.02.015] [Reference Citation Analysis]
3 Xie J, Zhang L. Machine learning and symbolic regression for adsorption of atmospheric molecules on low-dimensional TiO2. Applied Surface Science 2022;597:153728. [DOI: 10.1016/j.apsusc.2022.153728] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem Rev 2022. [PMID: 35862246 DOI: 10.1021/acs.chemrev.2c00061] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]