Page 39 - Read Online

Page 32 of 33 Liu et al. J Mater Inf 2024;4:33 https://dx.doi.org/10.20517/jmi.2024.48

112. K C A, Rao CS, Nair V. Combination of ensemble machine learning models in photocatalytic studies using nano TiO - Lignin based
2
biochar. Chemosphere 2024;352:141326. DOI PubMed
113. Wang Y, Sorkun MC, Brocks G, Er S. ML-aided computational screening of 2D materials for photocatalytic water splitting. J Phys
Chem Lett 2024;15:4983-91. DOI PubMed
114. Rashtbari S, Dehghan G, Marefat A, Khataee S, Khataee A. Proficient sonophotocatalytic degradation of organic pollutants using
Co O /TiO nanocomposite immobilized on zeolite: optimization, and artificial neural network modeling. Ultrason Sonochem
3
2
4
2024;102:106740. DOI PubMed PMC
115. Li Z, Li H, Meng L. Model compression for deep neural networks: a survey. Computers 2023;12:60. DOI
116. Fu C, Li H, Li W, et al. Rapid detection of trace sulfur content in ship fuel oil based on tin oxide quantum dot fluorescent sensors
assisted by multi-column convolutional neural network. Microchem J 2024;205:111396. DOI
117. Sethi S, Dhir A, Arora V. Photocatalysis based hydrogen production and antibiotic degradation prediction using neural networks.
Reac Kinet Mech Cat 2023;136:3283-97. DOI
118. Kakhki R, Zirjanizadeh S, Mohammadpoor M. A review of clinoptilolite, its photocatalytic, chemical activity, structure and
properties: in time of artificial intelligence. J Mater Sci 2023;58:10555-75. DOI
119. Isazawa T, Cole JM. How beneficial is pretraining on a narrow domain-specific corpus for information extraction about
photocatalytic water splitting? J Chem Inf Model 2024;64:3205-12. DOI PubMed PMC
120. Aid L, Abbou MS, Gafour AR, et al. Data-augmenting self-attention network for predicting photocatalytic degradation efficiency: a
study on TiO /curcumin nanocomposites. Reac Kinet Mech Cat 2024;137:3499-516. DOI
2
121. Li J, Shi H, Li Z, et al. Interaction of metal ions in high efficiency seawater hydrogen peroxide production by a carbon-based
photocatalyst. Appl Catal B Environ 2024;343:123541. DOI
122. Hayashi Y, Nagai Y, Pan Z, Katayama K. Convolutional neural network prediction of the photocurrent-voltage curve directly from
scanning electron microscopy images†. J Mater Chem A 2023;11:22522-32. DOI
123. Schmidt-Hieber J. The Kolmogorov-Arnold representation theorem revisited. Neural Netw 2021;137:119-26. DOI PubMed
124. Wang C, Tan X, Zhu B, et al. Deep learning-assisted non-invasive pediatric tic disorder diagnosis using EEG features extracted by
residual neural networks. J Radiat Res Appl Sci 2024;17:101151. DOI
125. He H, Wang Y, Qi Y, Xu Z, Li Y, Wang Y. From prediction to design: recent advances in machine learning for the study of 2D
materials. Nano Energy 2023;118:108965. DOI
126. Tao Q, Lu T, Sheng Y, Li L, Lu W, Li M. Machine learning aided design of perovskite oxide materials for photocatalytic water
splitting. J Energy Chem 2021;60:351-9. DOI
127. Huang M, Wang S, Zhu H. A comprehensive machine learning strategy for designing high-performance photoanode catalysts. J
Mater Chem A 2023;11:21619-27. DOI
128. Lin Z, Li Y, Haque SA, Ganose AM, Kafizas A. Insights from experiment and machine learning for enhanced TiO coated glazing for
2
photocatalytic NOx remediation†. J Mater Chem A 2024;12:13281-98. DOI
129. Tamtaji M, Guo X, Tyagi A, et al. Machine learning-aided design of gold core-shell nanocatalysts toward enhanced and selective
photooxygenation. ACS Appl Mater Interfaces 2022;14:46471-80. DOI PubMed
130. Miodyńska M, Mikolajczyk A, Mazierski P, et al. Lead-free bismuth-based perovskites coupled with g-C N : a machine learning
3
4
based novel approach for visible light induced degradation of pollutants. Appl Surf Sci 2022;588:152921. DOI
131. Chen F, Yang Y, Chen X. A first-principles and machine learning study on design of graphitic carbon nitride-based single-atom
photocatalysts. ACS Appl Nano Mater 2024;7:11862-70. DOI
132. Choudhary K, Garrity KF. InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning.
Digital Discov 2024;3:1365-77. DOI
133. Guevarra D, Zhou L, Richter MH, et al. Materials structure-property factorization for identification of synergistic phase interactions
in complex solar fuels photoanodes. npj Comput Mater 2022;8:747. DOI
134. Jiang Z, Hu J, Samia A, Yu X. Predicting active sites in photocatalytic degradation process using an interpretable molecular-image
combined convolutional neural network. Catalysts 2022;12:746. DOI
135. Zhou Y, Wang X, Huang X, Deng H, Hu Y, Lu L. Predicting the photosynthetic ammonia on nanoporous cobalt zirconate via graph
convolutional neural networks. Mol Catal 2022;529:112565. DOI
136. Bonke SA, Trezza G, Bergamasco L, et al. Multi-variable multi-metric optimization of self-assembled photocatalytic CO reduction
2
performance using machine learning algorithms. J Am Chem Soc 2024;146:15648-58. DOI PubMed PMC
137. Wang S, Mo P, Li D, Syed A. Intelligent algorithms enable photocatalyst design and performance prediction. Catalysts 2024;14:217.
DOI
138. Liu Y, Ge Q, Wang T, et al. Investigating the impact of pretreatment strategies on photocatalyst for accurate CO RR productivity
2
quantification: a machine learning approach. Chem Eng J 2023;473:145255. DOI
139. Jaffari ZH, Abbas A, Lam SM, et al. Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based
materials in the removal of malachite green. J Hazard Mater 2023;442:130031. DOI PubMed
140. Özsoysal S, Oral B, Yıldırım R. Analysis of photocatalytic CO reduction over MOFs using machine learning. J Mater Chem A
2
2024;12:5748-59. DOI
141. Yang Y, Zheng Y, Liu S, et al. Deep learning prediction of photocatalytic water splitting for hydrogen production under natural light
based on experiments. Energy Convers Manage 2024;301:118007. DOI