Machine learning analysis of photocatalytic CO2 reduction on perovskite materials
Materials Research Bulletin, cilt.188, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 188
- Basım Tarihi: 2025
- Doi Numarası: 10.1016/j.materresbull.2025.113436
- Dergi Adı: Materials Research Bulletin
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Chimica, Compendex, INSPEC
- Anahtar Kelimeler: Machine learning, Decision tree, Random forest, Band gap prediction, Photocatalytic CO2 reduction
- Boğaziçi Üniversitesi Adresli: Evet
Özet
A dataset containing 328 samples extracted from 66 experimental articles on photocatalytic CO2 reduction over perovskite materials was constructed and analyzed using machine learning. Random forest algorithm was used to predict total product yield in gas and liquid phase separately; decision tree algorithm was also utilized to deduce heuristic rules for high performance. Unavailable band gaps were also predicted using a linear regression trained by available data. Random forest models for both phases were quite successful. R2 and RMSE for liquid phase were 0.96 and 0.21, respectively for training (0.84 and 0.36 respectively for testing); for the gas phase, R2 and RMSE were 0.91 and 0.22 respectively for training (0.87 and 0.24 respectively for testing). The testing accuracy of decision tree models (0.88 % for gas and 0.73 % for liquid phases) were also reasonably high. The perovskite synthesis method was the most important descriptors for both RF and DT models.