The design and optimization of heterogeneous catalysts using computational methods

Shambhawi,; Mohan, Ojus; Choksi, Tej S.; Lapkin, Alexei A.

doi:10.1039/d3cy01160g

Cited by 6 publications

(2 citation statements)

References 166 publications

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“…In this context, advanced theory and simulation will play a significant role in identifying key variables and augmenting available data sets. As has been reported in recent studies, density functional theory shows relatively high predictive power and versatility in data generation and property evaluation, especially for ML developments . It is not only applied to individual catalytic mechanism studies in heterogeneous catalysis, but also employed to provide more catalysis data in complementary with experimental data sets, such as Open Catalysis and Catalysis-Hub .…”

Section: Insights Into Bond-cleavage Reactions and Beyondmentioning

confidence: 99%

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Zhang,

Smith,

et al. 2024

ACS Catal.

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Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C−X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C−X cleavage reactions that are common in production, upcycling, or reactive separation: the C−C cleavage in cyclopropyl alcohol, the C−H cleavage in hydroacylation reactions, the C−O cleavage in β-O-4 linkages, and the C−N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. We also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

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Section: Insights Into Bond-cleavage Reactions and Beyondmentioning

confidence: 99%

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Zhang,

Smith,

et al. 2024

ACS Catal.

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“…The development of heterogeneous catalysts has evolved to include increasingly complex systems, such as single-atom, 1,2 dual atom, [3][4][5][6] bimetallic, 7,8 and trimetallic alloy nanoparticle (NP) catalysts. 9 In particular, trimetallic alloys often exhibit promising catalytic properties due to the abundance of polymetallic active sites, controllable effects of the alloying on the electronic structure and activity of the catalyst [10][11][12] as well as their ability to catalyze reactions through qualitatively new mechanisms. 13,14 For example, trimetallic catalysts could be employed 9 in many processes deemed crucial for global economic development and sustainability, including the https://doi.org/10.26434/chemrxiv-2024-m3mln ORCID: https://orcid.org/0000-0002-5559-0066 Content not peer-reviewed by ChemRxiv.…”

Section: Introductionmentioning

confidence: 99%

Modeling Realistic Structures of Trimetallic Nanoalloy Catalysts Using Chemically Meaningful Descriptors

Subramanian,

Polynski,

Eswaran

et al. 2024

Preprint

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The computational design of alloy catalysts is hindered by the uncertainty in their structure and arrangement of constituent elements within the lattice of catalyst particles, i.e., their chemical ordering. Moreover, chemical ordering in alloy nanoparticles (nanoalloys) can be affected by the reaction temperature due to thermal disorder. In this study, we develop a method for realistic simulations of trimetallic alloy nanocrystallites with the lowest energy chemical ordering or chemical ordering taking into account thermal disorder in the nanoalloy. This method is based on Monte Carlo simulations using topological lattice Hamiltonian, whose parameters are fitted to the results of density functional (DFT) simulations of thoughtfully designed archetypal nanoalloy structures. The implementation of this method in Python code is freely available online. Using this method, we characterized chemical orderings in nanoparticles composed of 79 and 338 atoms of metals with known catalytic activity in CO2 hydrogenation, namely, Pd-Pt-Cu, Ni-Pd-Cu, and Co-Rh-Cu. Our simulations show that the thermal disorder in these alloys significantly affects the composition of surface sites. Such structural changes are demonstrated to affect the average binding energies of reaction intermediates to the catalyst surface by up to 1.1 eV, implying their critical effect on the alloy’s catalytic properties. Moreover, we demonstrate how the developed code can be used for brute-force evaluation of entropic contributions to mixing free energies in alloy nanoparticles. The demonstrated abilities of the proposed method to generate realistic models of trimetallic nanoalloys in a computationally efficient manner enable reliable simulations of catalytic properties of trimetallic catalysts for their in-depth understanding and computational design.

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