ReviewThe journal of physical chemistry. C, Nanomaterials and interfaces2024
Computational Design of Catalysts with Experimental Validation: Recent Successes, Effective Strategies, and Pitfalls.
Review in The journal of physical chemistry. C, Nanomaterials and interfaces, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Electrocatalysis and Photoelectrocatalysis: Perspective from Physicochemical Mechanisms to Applications.ACS omega · 2026Review
- URVAvis: A Graphical User Interface for the Visual Mechanistic Analysis of Chemical Reactions Based on the Unified Reaction Valley Approach.The journal of physical chemistry. A · 2026Article
- Few-Shot Ensemble Learning for Catalysis and Application to Trimetallics for Oxygen Reduction.ACS catalysis · 2026Article
- A Practical Guide to Heterogeneous Catalysis in Hydrocarbon Transformations.Chemistry, an Asian journal · 2025Review
- Enzymatic Hydroesterification of Waste Oils: Combilipase-Assisted Ethyl Ester Synthesis via a Dual-Step Biocatalytic Process.ACS omega · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computation has long proven useful in understanding heterogeneous catalysts and rationalizing experimental findings. However, computational design with experimental validation requires somewhat different approaches and has proven more difficult. In recent years, there have been increasing successes in such computational design with experimental validation. In this Perspective, we discuss some of these recent successes and the methodologies used. We also discuss various design strategies more broadly, as well as approximations to consider and pitfalls to try to avoid when designing for experiment. Overall, computation can be a powerful and efficient tool in guiding catalyst design but must be combined with a strong fundamental understanding of catalysis science to be most effective in terms of both choosing the design methodology and choosing which materials to pursue experimentally.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.