Issue 3, 2024

The design and optimization of heterogeneous catalysts using computational methods

Abstract

The computational design of catalytic materials is a high dimensional structure optimization problem that is limited by the bottleneck of expensive quantum computation tools. Current implementations of first principles computational models for catalyst design are data-hungry, problem-specific and confirmatory in nature. However, they can be made less data-dependent, more transferable and exploratory by developing both forward and inverse catalyst mapping tools that are either inexpensive correlations, like scaling relations, or regression models that are based on relevant descriptors analysis. This work reviews the current application and the possible landscape for future advancements of such tools for developing generalized schemes for catalyst design and optimization.

Graphical abstract: The design and optimization of heterogeneous catalysts using computational methods

Article information

Article type
Review Article
Submitted
19 Aug 2023
Accepted
01 Dec 2023
First published
01 Dec 2023
This article is Open Access
Creative Commons BY license

Catal. Sci. Technol., 2024,14, 515-532

The design and optimization of heterogeneous catalysts using computational methods

Shambhawi, O. Mohan, T. S. Choksi and A. A. Lapkin, Catal. Sci. Technol., 2024, 14, 515 DOI: 10.1039/D3CY01160G

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements