Optimization of chemical synthesis with heuristic algorithms.

Area of Science:

• Chemical Synthesis
• Computational Chemistry
• Process Optimization

Background:

• Optimizing reaction conditions is crucial for chemical synthesis and industrial processes.
• Experimental exploration of all possible reaction conditions is infeasible.
• Bayesian optimization has shown promise in outperforming human decision-making for reaction optimization.

Purpose of the Study:

• To optimize reaction conditions for Buchwald-Hartwig and Suzuki coupling reactions.
• To evaluate the performance of heuristic algorithms and encoding methods in predicting reaction yields.
• To identify a computationally efficient and practical optimization strategy for chemical synthesis.

Main Methods:

• Utilized three heuristic algorithms: particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA).
• Employed three encoding methods for representing reaction conditions.
• Predicted reaction yields for Buchwald-Hartwig and Suzuki systems using the selected algorithms and encodings.

Main Results:

• Particle swarm optimization (PSO) with numerical encoding demonstrated superior performance compared to GA and SA.
• The performance of PSO was found to be comparable to Bayesian optimization.
• PSO achieved this performance without the high computational costs associated with descriptor calculations.

Conclusions:

• Particle swarm optimization (PSO) with numerical encoding is a highly effective method for optimizing chemical reaction conditions.
• PSO offers a practical and computationally efficient alternative to Bayesian optimization for promoting chemical synthesis.
• The simplicity and ease of implementation of PSO make it suitable for integration into laboratory practices.