Dean, James
(2021)
Computational Design of Optimal Bimetallic Nanoparticles: Bridging Stability with Adsorption.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
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Unpublished |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: |
|
| ETD Committee: |
|
| 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 2023 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/40182 |
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