Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P–H Reactions
Abstract
:1. Introduction
2. Results and Discussion
3. Experimental Section
3.1. Data Source
3.2. Quantum Chemistry Calculations and Descriptor Acquisition
3.3. Machine Learning Models
3.3.1. Partial Least Squares Regression (PLSR)
3.3.2. Multiple Linear Regression (MLR)
3.3.3. Stepwise Multiple Linear Regression (SMLR)
3.3.4. Artificial Neural Networks (ANN)
3.3.5. Support Vector Machine (SVM)
3.4. Synthesis
3.5. Structural Validation of Synthesized Product
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Sample Availability
References
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Ma, Y.; Zhang, X.; Zhu, L.; Feng, X.; Kowah, J.A.H.; Jiang, J.; Wang, L.; Jiang, L.; Liu, X. Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P–H Reactions. Molecules 2023, 28, 5995. https://doi.org/10.3390/molecules28165995
Ma Y, Zhang X, Zhu L, Feng X, Kowah JAH, Jiang J, Wang L, Jiang L, Liu X. Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P–H Reactions. Molecules. 2023; 28(16):5995. https://doi.org/10.3390/molecules28165995
Chicago/Turabian StyleMa, Youfu, Xianwei Zhang, Lin Zhu, Xiaowei Feng, Jamal A. H. Kowah, Jun Jiang, Lisheng Wang, Lihe Jiang, and Xu Liu. 2023. "Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P–H Reactions" Molecules 28, no. 16: 5995. https://doi.org/10.3390/molecules28165995