Machine Learning Applications for Chemical Reactions.

Area of Science:

• Computational Chemistry
• Chemical Informatics
• Artificial Intelligence in Chemistry

Background:

• Machine learning (ML) excels in molecular property prediction and materials design.
• Traditional methods for chemical reaction analysis are often costly and time-consuming.
• ML techniques are increasingly applied to complex chemical reaction problems.

Purpose of the Study:

• To review recent advancements in ML applications for chemical reaction studies.
• To summarize ML strategies for predicting reaction properties and synthetic routes.
• To highlight ML's potential in overcoming limitations of experimental and simulation methods.

Main Methods:

• Utilizing various ML models for property prediction.
• Applying ML to analyze chemical reaction data.
• Exploring ML for retrosynthetic pathway design.

Main Results:

• ML models accurately predict physical properties like thermodynamic changes, activation barriers, and reaction rates.
• ML approaches demonstrate success in predicting reactivity and enabling reaction self-optimization.
• ML aids in designing retrosynthetic reaction paths.

Conclusions:

• ML offers powerful and efficient solutions for diverse chemical reaction challenges.
• ML strategies are transforming the study and application of chemical reactions.
• This review provides insights into current ML methodologies in reaction chemistry.