Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis
Miguel Steiner, Markus Reiher

TL;DR
This paper discusses the development of autonomous computational methods for reaction network exploration in catalysis, enabling systematic, unbiased, and efficient analysis of complex catalytic processes with potential for new discoveries.
Contribution
It introduces a framework for autonomous reaction network exploration in catalysis, integrating fast electronic structure methods and addressing conceptual challenges.
Findings
Automated exploration can consider vastly more structures than manual methods.
High efficiency and reliability enable predictive catalysis modeling.
Autonomous methods facilitate unbiased, high-fidelity analysis with potential for surprising discoveries.
Abstract
Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) Automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy…
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