Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Area of Science:

• Computational chemistry
• Catalysis
• Reaction network elucidation

Background:

• Computational catalysis currently relies heavily on manual exploration.
• Existing methods are limited in the scope of structures and reactions that can be analyzed.
• Human bias and errors can influence computational findings.

Purpose of the Study:

• To introduce and review autonomous computation for reaction network elucidation in catalysis.
• To highlight the advantages of automated approaches over manual methods.
• To discuss conceptual challenges and potential of autonomous procedures in computational catalysis.

Main Methods:

• Review of state-of-the-art computational catalysis methods.
• Elaboration on conceptual issues in autonomous computational procedures.
• Application to an example catalytic system for illustration.

Main Results:

• Autonomous algorithms allow systematic exploration of orders of magnitude more structures and reaction steps.
• Fast electronic structure methods with uncertainty quantification ensure efficiency and reliability.
• Reduced manual work, errors, and bias enhance the fidelity of computational catalysis.

Conclusions:

• Autonomous computations are poised to elevate computational catalysis to the level of experimental research.
• This approach facilitates comprehensive analysis and unbiased discovery of catalytic processes.
• Further development and application of autonomous procedures promise significant advancements in the field.