Journal: Current Organic Chemistry
Guest editor(s):Dr. Qiang Zhang
Co-Guest Editor(s):

Introduction

This research area combines theoretical computation and screening with machine learning for the design of covalent/metal organic framework-based catalysts, bridging the disciplines of organic chemistry, physical chemistry, computational chemistry, materials science, and machine learning. It covers several critical aspects: designing and synthesizing organic catalysts for improved performance, applying computational methods and machine learning to gain deeper insights into organic catalysts behavior and reaction mechanisms, using these tools to predict electronic structures and reaction pathways more accurately, and optimizing organ catalytic reactions. Machine learning's data analysis capabilities accelerate catalyst design, fostering innovation in organic catalysts. Additionally, it offers comprehensive assessments of catalytic activity and reaction selectivity, aiding in catalyst selection and reaction optimization. This interdisciplinary research field is poised to shape the future of organic chemistry and catalysis, enabling eco-friendly processes and the discovery of novel compounds.

Keywords

Organic catalysts, Machine learning, Computational chemistry, Reaction mechanisms, Covalent/metal organic framework

Sub-topics