BiDir-GRCO: A Bidirectional General Reaction Conditions Optimization Framework Integrating Multi-Armed Bandit and...

Area of Science:

• Chemical Synthesis
• Computational Chemistry
• Process Optimization

Background:

• Optimizing reaction conditions in chemical synthesis is challenging due to multiple interacting factors (catalysts, solvents, temperature, time).
• Optimal conditions for one substrate often do not translate to others, limiting generalizability in research and industrial production.

Purpose of the Study:

• To develop a bidirectional general reaction condition optimization framework.
• To improve the accuracy and adaptability of optimizing reaction conditions across diverse chemical substrates and conditions.

Main Methods:

• Integration of the multiarmed bandit algorithm for dynamic exploration-exploitation in condition selection.
• Application of a regression model with molecular representation and per-substrate selective training for substrate selection.
• Development of a bidirectional framework for simultaneous optimization of conditions and substrate selection.

Main Results:

• The framework demonstrated high efficiency and strong adaptability across various reaction datasets.
• Achieved accuracy improvements of 20% and 15% over state-of-the-art models on comparable datasets.
• Maintained robust optimization performance on specialized datasets with extensive substrate and condition combinations.

Conclusions:

• The proposed bidirectional framework effectively optimizes general reaction conditions in chemical synthesis.
• The integration of multiarmed bandit and regression models enhances accuracy and adaptability.
• This approach offers a significant advancement for both research and industrial chemical production.