A human-machine interface for automatic exploration of chemical reaction networks

Area of Science:

• Computational Chemistry
• Chemical Kinetics
• Catalysis

Background:

• Autonomous reaction network exploration algorithms are crucial for understanding complex chemical processes.
• Existing methods face computational challenges due to vast reaction networks and lack flexibility in defining intermediates or constraints.
• Brute-force approaches are computationally infeasible, while pre-defined constraints limit applicability.

Purpose of the Study:

• To introduce a novel algorithm, STEERING WHEEL, for guiding automated reaction network exploration.
• To provide an intuitive and generally applicable method for focusing exploration on specific network regions.
• To enable flexible guidance of automated data generation for mechanism exploration and catalyst design.

Main Methods:

• Development of the STEERING WHEEL algorithm for unbiased automated exploration.
• Application of the algorithm to reaction mechanism elucidation of transition metal catalysts.
• Demonstration of systematic and reproducible exploration of catalytic cycles.

Main Results:

• The STEERING WHEEL algorithm effectively guides automated exploration of vast reaction networks.
• It allows for adjustable exploration objectives, accommodating both accurate calculations and high-throughput screening.
• The method facilitates focused investigation of specific regions within emerging chemical networks.

Conclusions:

• The STEERING WHEEL algorithm offers a flexible and efficient solution for navigating complex chemical reaction spaces.
• It enhances the study of catalytic cycles and aids in catalyst design and mechanism elucidation.
• This approach overcomes limitations of previous methods by providing adjustable guidance for automated chemical exploration.