<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-20T23:00:05Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/124051" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/124051</identifier><datestamp>2026-06-16T18:54:50Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131022</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Troy Van Voorhis.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Ricke, Nathan Darrell Peterson.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Department of Chemistry.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department" lang="en_US">Massachusetts Institute of Technology. Department of Chemistry</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2020-03-09T18:51:08Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2020-03-09T18:51:08Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2019</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2019</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1721.1/124051</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">1142099325</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 2019</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (pages 79-87).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Computational modeling has untapped potential for novel material and chemical discovery. In this thesis, we explore ways to improve existing modeling methods, and how to apply these methods to design novel graphite-conjugated catalysts (GCCs). For improving electronic structure methods, we first present an extended study of bootstrap embedding theory (BET) and its ability to recover static correlation, as well as a proof on BET's ideal convergence properties. We then present a theoretical analysis using density functional theory (DFT) on a class of GCCs containing cationic nitrogen atoms, which are particularly active for catalyzing the oxygen reduction reaction (ORR). Using a mixture of high-throughput screening, statistical analysis, and computational exploration guided by chemical intuition, we design several novel GCCs, several of which DFT predicts would have enhanced activity above existing GCCs. Furthermore, our analysis reveals that known ORR scaling relations hold for GCCs, but hint at the possibility of breaking these relations with careful molecular engineering of the GCC active sites.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Nathan Darrell Peterson Ricke.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">Ph.D.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="collection" lang="en_US">Ph.D. Massachusetts Institute of Technology, Department of Chemistry</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">87 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Chemistry.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Development of electronic structure and kinetics methods for the rational design of electrocatalysts</dim:field>
   <dim:field mdschema="dc" element="type" lang="en_US">Thesis</dim:field>
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   <dim:field mdschema="mit" element="thesis" qualifier="degree" lang="en_US">Doctoral</dim:field>
   <dim:field mdschema="mit" element="thesis" qualifier="department" lang="en_US">Chem</dim:field>
   <dim:field mdschema="others" element="access-status">unknown</dim:field>
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   	&lt;Title>Development of electronic structure and kinetics methods for the rational design of electrocatalysts&lt;/Title>
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   	&lt;PublicationDate>2019&lt;/PublicationDate>
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    &lt;Keyword>Chemistry.&lt;/Keyword&gt;
   	&lt;Abstract>Computational modeling has untapped potential for novel material and chemical discovery. In this thesis, we explore ways to improve existing modeling methods, and how to apply these methods to design novel graphite-conjugated catalysts (GCCs). For improving electronic structure methods, we first present an extended study of bootstrap embedding theory (BET) and its ability to recover static correlation, as well as a proof on BET&amp;apos;s ideal convergence properties. We then present a theoretical analysis using density functional theory (DFT) on a class of GCCs containing cationic nitrogen atoms, which are particularly active for catalyzing the oxygen reduction reaction (ORR). Using a mixture of high-throughput screening, statistical analysis, and computational exploration guided by chemical intuition, we design several novel GCCs, several of which DFT predicts would have enhanced activity above existing GCCs. Furthermore, our analysis reveals that known ORR scaling relations hold for GCCs, but hint at the possibility of breaking these relations with careful molecular engineering of the GCC active sites.&lt;/Abstract>
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