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Accelerated Discovery of New Materials for Oxygen Evolution Reaction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Accelerated Discovery of New Materials for Oxygen Evolution Reaction./
作者:	Min, Yimeng.
面頁冊數:	1 online resource (75 pages)
附註:	Source: Masters Abstracts International, Volume: 81-04.
Contained By:	Masters Abstracts International81-04.
標題:	Computational chemistry. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13856743click for full text (PQDT)
ISBN:	9781085778671

Accelerated Discovery of New Materials for Oxygen Evolution Reaction.
Min, Yimeng.

Accelerated Discovery of New Materials for Oxygen Evolution Reaction. - 1 online resource (75 pages)

Source: Masters Abstracts International, Volume: 81-04.

Thesis (M.A.S.)--University of Toronto (Canada), 2019.

Includes bibliographical references

This thesis investigates new electrocatalysts for the oxygen evolution reaction, which is critical for the feasibility of water electrolysis. The evolution reaction can be used to store renewable energy that is produced intermittently. The discovery of new electrocatalysts is hindered by the vast chemical space. By developing a high-throughput method, I first targeted a group of promising crystal structures and narrowed the searching space for the oxygen evolution reaction using computational screening based on density functional theory. A machine learning algorithm was then implemented based on the database constructed. The 1st ionization energy and the atomic number were found to be accurate descriptors for the adsorption energy of OH and OOH. I used these two parameters as input for the random forest regressor for predicting the adsorption energies on doping materials. This method has thus far identified around 400 doping materials for a single type of crystal structure. The predicted catalyst properties agree with the reported experimental results and accelerate finding the top candidates for experimental validation. I validated the predicted results with reported data to ensure that the models are grounded in practice and consistent with experiments.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9781085778671Subjects--Topical Terms:

3350019
Computational chemistry.
Subjects--Index Terms:

CatalysisIndex Terms--Genre/Form:

542853
Electronic books.

Accelerated Discovery of New Materials for Oxygen Evolution Reaction.
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504 $a Includes bibliographical references
520 $a This thesis investigates new electrocatalysts for the oxygen evolution reaction, which is critical for the feasibility of water electrolysis. The evolution reaction can be used to store renewable energy that is produced intermittently. The discovery of new electrocatalysts is hindered by the vast chemical space. By developing a high-throughput method, I first targeted a group of promising crystal structures and narrowed the searching space for the oxygen evolution reaction using computational screening based on density functional theory. A machine learning algorithm was then implemented based on the database constructed. The 1st ionization energy and the atomic number were found to be accurate descriptors for the adsorption energy of OH and OOH. I used these two parameters as input for the random forest regressor for predicting the adsorption energies on doping materials. This method has thus far identified around 400 doping materials for a single type of crystal structure. The predicted catalyst properties agree with the reported experimental results and accelerate finding the top candidates for experimental validation. I validated the predicted results with reported data to ensure that the models are grounded in practice and consistent with experiments.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computational chemistry. $3 3350019
650 4 $a Chemical engineering. $3 560457
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653 $a Catalysis
653 $a High Throughput Screening
653 $a Machine Learning
653 $a Oxygen Evolution Reaction
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