Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

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 reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

78 pages, 10 figures

References in corpus (13)

• The Density Matrix Renormalization Group in Chemistry and Molecular Physics: Recent Developments and New Challenges
• Beyond scaling relations for the description of catalytic materials
• Context-Driven Exploration of Complex Chemical Reaction Networks
• OPTIMADE, an API for exchanging materials data
• Interactive Chemical Reactivity Exploration
• Common workflows for computing material properties using different quantum engines
• Automated construction of quantum-classical hybrid models
• Solvation Free Energies in Subsystem Density Functional Theory
• Enhancing automated reaction discovery with boxed molecular dynamics in energy space
• On the Predictive Power of Chemical Concepts
• JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
• Defining Autocatalysis in Chemical Reaction Networks
• TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

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