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Searching for Singlet Fission Candidates with Many-Body Perturbation Theory and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Searching for Singlet Fission Candidates with Many-Body Perturbation Theory and Machine Learning./
作者:	Liu, Xingyu.
面頁冊數:	1 online resource (243 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Condensed matter physics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28969370click for full text (PQDT)
ISBN:	9798209780403

Searching for Singlet Fission Candidates with Many-Body Perturbation Theory and Machine Learning.
Liu, Xingyu.

Searching for Singlet Fission Candidates with Many-Body Perturbation Theory and Machine Learning. - 1 online resource (243 pages)

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

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

Includes bibliographical references

Singlet fission (SF) is a photophysical process where one singlet-state exciton converts into two triplet-state excitons. SF is considered as a possible approach to surpass the Shockley-Queisser limit and has started wide discussions in the past decade. However, commercialization of SF-based photovoltaics remains in incubation due to the lack of practical SF materials. To tackle this bottleneck, performing large-scale simulation and screening molecular materials database to search for SF candidates with promising properties is suggested. One of the decisive excitonic properties directing the fission process, the SF thermodynamic driving force can be calculated with the state-of-the-art, many-body perturbation theory (MBPT) under the GW approximation paired with Bethe-Salpeter equation (BSE). However, GW+BSE calculation is too cumbersome to be selected as the screening scheme for a database with tens of thousands of molecular crystals. Statistical inference is hence introduced to maximize the probability of discovering SF candidates with minimized computational cost. To realize this process, a hierarchical screening workflow incorporating materials science and machine learning (MatML Workflow) was designed and implemented.

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

Mode of access: World Wide Web

ISBN: 9798209780403Subjects--Topical Terms:

3173567
Condensed matter physics.
Subjects--Index Terms:

Density functional theoryIndex Terms--Genre/Form:

542853
Electronic books.

Searching for Singlet Fission Candidates with Many-Body Perturbation Theory and Machine Learning.
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520 $a Singlet fission (SF) is a photophysical process where one singlet-state exciton converts into two triplet-state excitons. SF is considered as a possible approach to surpass the Shockley-Queisser limit and has started wide discussions in the past decade. However, commercialization of SF-based photovoltaics remains in incubation due to the lack of practical SF materials. To tackle this bottleneck, performing large-scale simulation and screening molecular materials database to search for SF candidates with promising properties is suggested. One of the decisive excitonic properties directing the fission process, the SF thermodynamic driving force can be calculated with the state-of-the-art, many-body perturbation theory (MBPT) under the GW approximation paired with Bethe-Salpeter equation (BSE). However, GW+BSE calculation is too cumbersome to be selected as the screening scheme for a database with tens of thousands of molecular crystals. Statistical inference is hence introduced to maximize the probability of discovering SF candidates with minimized computational cost. To realize this process, a hierarchical screening workflow incorporating materials science and machine learning (MatML Workflow) was designed and implemented.
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538 $a Mode of access: World Wide Web
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