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Computational Design of Optimal Bimetallic Nanoparticles: Bridging Stability with Adsorption

Dean, James (2021) Computational Design of Optimal Bimetallic Nanoparticles: Bridging Stability with Adsorption. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

In the 20th century, advancements in computational power and chemical theory revolutionized catalyst discovery. Now in the 21st century, machine learning techniques have started making their way into the toolbox of computational chemistry, accelerating material design and discovery.
In this dissertation, we take a two-pronged approach to advance the current state-of-the-art when it comes to rational catalyst design. We begin by illuminating the fundamental physical properties relevant to the adsorption of small molecules to nanoparticles, arriving at a set of universal adsorption descriptors with the use of Density-Functional Theory calculations and machine learning. We then develop CE Expansion, a new open-source genetic algorithm for the rapid optimization of the bimetallic mixing behavior in nanoparticles of any size, shape, or composition, and use this to create the MetalNanoDB, a database of over 5,400 low-energy nanoparticles. Finally, we bring together these two approaches, and demonstrate a new workflow for the high-throughput screening of potential nanocatalysts for their physical properties, targeting CO2 adsorption as a proof-of-concept (relevant to mitigating the greenhouse effect).
Overall, this work accelerates catalyst design by developing tools that rapidly and accurately model bimetallic nanocatalysts, and efficiently sieve the tremendously large nanomaterials space for targeted catalytic applications.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Dean, Jamesjrd101@pitt.edujrd1010000-0003-0932-7707
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMpourmpakis, Giannisgmpourmp@pitt.edugmpourmp0000-0002-3063-0607
Committee MemberWilmer, Christopherwilmer@pitt.eduwilmer0000-0002-7440-5727
Committee MemberVeser, Götzgveser@pitt.edugveser0000-0002-2084-4636
Committee MemberMillstone, Jilljem210@pitt.edujem2100000-0002-9499-5744
Date: 13 June 2021
Date Type: Publication
Defense Date: 14 December 2020
Approval Date: 13 June 2021
Submission Date: 19 January 2021
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 199
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical and Petroleum Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Nanoparticles, Machine Learning, Density-Functional Theory, CO2, Adsorption, Catalysis, Computational Chemistry
Date Deposited: 13 Jun 2021 17:44
Last Modified: 13 Jun 2021 17:44
URI: http://d-scholarship.pitt.edu/id/eprint/40182

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