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Machine Learning and Ab Initio Insig...

Kharabadze, Saba.

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Machine Learning and Ab Initio Insights into the Design of Lithium-Based Materials.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning and Ab Initio Insights into the Design of Lithium-Based Materials./
Author:	Kharabadze, Saba.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	159 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Physics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31296909
ISBN:	9798383135587

Machine Learning and Ab Initio Insights into the Design of Lithium-Based Materials.
Kharabadze, Saba.

Machine Learning and Ab Initio Insights into the Design of Lithium-Based Materials. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 159 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2024.

Materials research, spanning physics, chemistry, and engineering, underpins technological innovation by enabling precise control of materials at the atomistic level. Nevertheless, the challenges of exploring the vast chemical space efficiently and cost-effectively remain. Quantum mechanical methods, such as density functional theory (DFT), while theoretically powerful, often prove computationally expensive for large systems. This thesis showcases the development of accurate machine learning potentials which aid in prediction of new materials. I have chosen two main studies to highlight the work conducted during my PhD research. In the investigation of the Li-Sn binary system, which has been considered for energy storage applications, we focused on identifying new stable crystal structures. After constructing an interatomic description model for Li-Sn, we introduced a protocol for finding stable compounds at both low and high temperatures. Although this binary had been thoroughly explored in two recent ab initio predictive studies, we found two overlooked compounds thermodynamically stable at ambient conditions. Building on this proof-of-principle study, we expanded the scope of investigation to a much larger set of binaries to check the protocol's general performance. The investigated M-Sn binaries, where M = Na, Ca, Cu, Pd, and Ag, have a potential for a wider range of applications in energy storage, electronics packaging, and superconductivity. The application of the developed approach has led to the identification of a large number of stable phases at various synthesis conditions. The findings of these two studies demonstrated a great promise, as the discovery of over 30 new stable crystal structures has dramatically increased the number of successful predictions based on machine learning potentials. The third study was dedicated to the analysis of thermodynamic stability of Li-B-C compounds, that had been attracting a lot of attention as potential record-breaking conventional superconductors. We utilized DFT to characterize both well-established and recently reported Li-B-C phases. Our work included demonstration and rationalization of inconsistencies in previously proposed crystal structure solutions. One of the main outcomes is the construction of the phase diagram of the LiBC delithiation. Our findings reveal an incomplete knowledge of the Li-B-C ternary and the need for further experimental exploration of this intriguing materials system.

ISBN: 9798383135587Subjects--Topical Terms:

516296
Physics.
Subjects--Index Terms:

Machine learning

Machine Learning and Ab Initio Insights into the Design of Lithium-Based Materials.
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