%V_Bur index and steric maps: from predictive catalysis to machine learning

Area of Science:

• Chemistry
• Computational Chemistry
• Chemical Engineering

Background:

• Steric indices describe molecular spatial arrangements, influencing reactivity, stability, and physical properties.
• Steric hindrance and steric bulk are key concepts, with indices like Tolman cone angle and %V_Bur quantifying these effects.
• Electronic indices, while common, have limitations in computational cost and predictive power compared to steric measures.

Purpose of the Study:

• To highlight the importance of steric indices in understanding chemical behavior.
• To compare the utility of steric indices, particularly %V_Bur, against electronic indices.
• To emphasize the role of steric indices in advancing predictive chemistry and catalysis for sustainability.

Main Methods:

• Review and discussion of steric indices, including steric hindrance and steric bulk.
• Comparison of steric indices (e.g., %V_Bur) with electronic indices in chemical property prediction.
• Exploration of the application of steric indices in predictive catalysis.

Main Results:

• Steric indices effectively predict reactivity, stability, and physical properties of chemical compounds.
• %V_Bur demonstrates advantages over electronic indices in certain predictive applications, potentially due to inherent electronic content.
• Steric indices are crucial for developing efficient and sustainable predictive catalysis.

Conclusions:

• Steric indices are vital tools for predicting chemical compound behavior and designing new molecules.
• %V_Bur offers a valuable metric for predictive catalysis, enhancing efficiency and sustainability.
• Integrating steric insights into computational methods can minimize waste and experimental effort in chemical research.