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Discovery of Novel Low Dimensional and Photocathode Materials Using Materials Data and Simulations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Discovery of Novel Low Dimensional and Photocathode Materials Using Materials Data and Simulations./
作者:	Antoniuk, Evan Ronald William.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	155 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Crystal structure. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812972
ISBN:	9798494454362

Discovery of Novel Low Dimensional and Photocathode Materials Using Materials Data and Simulations.
Antoniuk, Evan Ronald William.

Discovery of Novel Low Dimensional and Photocathode Materials Using Materials Data and Simulations. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 155 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Utilizing computational methods for uncovering new materials with desirable properties is a powerful tool for materials discovery. The grand goal of these approaches is to efficiently search through all of material space in order to find the best candidates for any application. Compared to traditional "trial-and-error" based materials discovery approaches, computational materials discovery allows for: i) a diverse range of materials can be explored, beyond the already successful spaces, ii) easier optimization over multiple performance metrics, and iii) statistically motivated insights into the materials space can be derived. However, discovering materials in this manner is a very difficult optimization problem that can involve assessing hundreds of thousands of candidate materials according to multiple selection criteria. Understanding how to best utilize these materials datasets is therefore an open problem that requires the development of screening methodologies. In this dissertation, I explore the use of data-driven materials discovery methods for discovering new assembly-free van der Waals heterostructures and high brightness photocathode materials. Additionally, I will discuss the development new machine-learning methodologies to improve the efficiency and success of computationalbased material discovery efforts. Van der Waals heterostructures are layered materials that consist of vertical stacks of weakly bound two-dimensional (2D) layers. Since these 2D layers are held together through van der Waals interactions, a nearly endless number of unique van der Waals heterostructure can be synthesized, thus giving rise to a massive space for material design. However, the experimental synthesis of these van der Waals heterostructures can be challenging. One of the most common methods for synthesizing van der Waals heterostructures involves the slow and imprecise stacking of individual 2D layers that have been obtained from the mechanical exfoliation of a bulk layered material. An alternative synthetic approach is to mechanically exfoliate a bulk crystal that is composed of vertical stacks of chemically dissimilar layers. The main limitation of this approach is the small number of known, bulk crystals with vertical stacks of dissimilar layers. To this end, I have utilized a data mining approach to identify all weakly bonded layered materials in the Materials Project database. I then utilize density functional theory calculations to determine the properties of these data-mined materials and discover a subset that are promising candidates for photovoltaic and light-emitting diode applications. Next, I will discuss my work on the computational discovery of novel photocathode materials for increasing the brightness of x-ray free-electron laser (XFEL) light sources. Previous efforts to computationally discover photocathode materials has been challenging as previous photoemission models have not been developed in a general way, preventing them from being applied to universally predict the emission properties of photocathode materials. Therefore, to enable the computational discovery of novel photocathode materials, I have developed a generalizable density functional theory based photoemission model. We benchmark this model across a broad spectrum of photoemitting materials and find that it has a mean absolute error that is ~5x less than previously derived expressions. Following the development of this model, I have then used this model to screen for novel high brightness photocathode materials. These screening efforts culminate in the discovery of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. Additionally, multi-objective screening is employed to identify the family of M2O (M=Na,K,Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability.

ISBN: 9798494454362Subjects--Topical Terms:

3561040
Crystal structure.

Discovery of Novel Low Dimensional and Photocathode Materials Using Materials Data and Simulations.
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