Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P-H Reactions.
Molecules
; 28(16)2023 Aug 10.
Article
em En
| MEDLINE
| ID: mdl-37630247
ABSTRACT
The paper discussed the use of machine learning (ML) and quantum chemistry calculations to predict the transition state and yield of copper-catalyzed P-H insertion reactions. By analyzing a dataset of 120 experimental data points, the transition state was determined using density functional theory (DFT). ML algorithms were then applied to analyze 16 descriptors derived from the quantum chemical transition state to predict the product yield. Among the algorithms studied, the Support Vector Machine (SVM) achieved the highest prediction accuracy of 97%, with over 80% correlation in Leave-One-Out Cross-Validation (LOOCV). Sensitivity analysis was performed on each descriptor, and a comprehensive investigation of the reaction mechanism was conducted to better understand the transition state characteristics. Finally, the ML model was used to predict reaction plans for experimental design, demonstrating strong predictive performance in subsequent experimental validation.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Molecules
Assunto da revista:
BIOLOGIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
CH
/
SUIZA
/
SUÍÇA
/
SWITZERLAND