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Deep Learning Methods for Catalyst Surface and Interface Structure Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning Methods for Catalyst Surface and Interface Structure Analysis./
作者:	Yoon, Junwoong.
面頁冊數:	1 online resource (178 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Chemical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29169545click for full text (PQDT)
ISBN:	9798438724773

Deep Learning Methods for Catalyst Surface and Interface Structure Analysis.
Yoon, Junwoong.

Deep Learning Methods for Catalyst Surface and Interface Structure Analysis. - 1 online resource (178 pages)

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2022.

Includes bibliographical references

The increase in global energy demand and raised environmental concerns have motivated the design of novel materials for energy-related applications. However, the design of ever-complicating materials for emerging energy technologies is currently bottlenecked by limited resources to understand complex surface and interface structures and property relationships. In the first part of this thesis, we develop a tandem framework that combines a molecular thermodynamic theory and molecular dynamics simulations in an attempt to investigate solid interfacial phenomena and to discuss how deep learning methods can improve the framework as a next step. In the second part, we develop a set of deep learning methods that solve various materials and catalyst design problems including property, structure, and stability analysis. We present a graph neural networks architecture to learn the optimal representations of heterogeneous catalysis systems for the accurate prediction of adsorption/binding energies. Then we extended the approach to approximate ground-state structures of the catalysis systems by incorporating differentiable optimization methods into the graph neural networks architecture. We further develop a general deep reinforcement learning framework to identify the metastability of alloy catalyst surfaces by exploring possible surface reconstructions and their associated kinetic barriers under reaction conditions. With these advanced data-driven methods that understand the surface and interfacial phenomena, we open up new avenues for accelerated materials and catalyst discovery.

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

Mode of access: World Wide Web

ISBN: 9798438724773Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Catalyst designIndex Terms--Genre/Form:

542853
Electronic books.

Deep Learning Methods for Catalyst Surface and Interface Structure Analysis.
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538 $a Mode of access: World Wide Web
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