ArticleTopics in catalysis2022
Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis.
Article in Topics in catalysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Utility-Scale Quantum Computational Chemistry.The journal of physical chemistry letters · 2026Review
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Reactive machine learning potential for accelerating transition state search in organic synthesis.Nature communications · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Chemistry in Extreme Environments: The Mystery of Molecular Complexity in Space.ACS central science · 2026Review
- Exploring chemistry and catalysis by biasing skewed distributions via deep learning.Nature communications · 2026Article
- Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations.Journal of chemical theory and computation · 2025Article
- Automated Microsolvation for Minimum Energy Path Construction in Solution.Journal of chemical theory and computation · 2025Article
- 3T-VASP: fast ab-initio electrochemical reactor via multi-scale gradient energy minimization.Nature communications · 2024Article
- Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks.The journal of physical chemistry. A · 2024Article
- Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States.The journal of physical chemistry letters · 2024Article
- Nanoscale chemical reaction exploration with a quantum magnifying glass.Nature communications · 2024Article
- Uncertainty-Aware First-Principles Exploration of Chemical Reaction Networks.The journal of physical chemistry. A · 2024Article
- A human-machine interface for automatic exploration of chemical reaction networks.Nature communications · 2024Article
- Deep reaction network exploration of glucose pyrolysis.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application.Journal of chemical information and modeling · 2023Article
- Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning.Digital discovery · 2023Article
- LinChemIn: SynGraph-a data model and a toolkit to analyze and compare synthetic routes.Journal of cheminformatics · 2023Article
- Comprehensive exploration of graphically defined reaction spaces.Scientific data · 2023Article
- Pathfinder─Navigating and Analyzing Chemical Reaction Networks with an Efficient Graph-Based Approach.Journal of chemical information and modeling · 2023Article
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2 authors.
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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. GRAPHICAL ABSTRACT: SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11244-021-01543-9.
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