東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine Learning and Ab Initio Insig...

Kharabadze, Saba.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning and Ab Initio Insights into the Design of Lithium-Based Materials./
作者:	Kharabadze, Saba.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	159 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Physics. -
電子資源:	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.
LDR:03664nmm a2200385 4500 001 2403671
005 20241118135910.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798383135587
035 $a (MiAaPQ)AAI31296909
035 $a AAI31296909
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kharabadze, Saba. $0 (orcid)0000-0003-4504-4181 $3 3773940
245 1 0 $a Machine Learning and Ab Initio Insights into the Design of Lithium-Based Materials.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 159 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Kolmogorov, Alexey.
502 $a Thesis (Ph.D.)--State University of New York at Binghamton, 2024.
520 $a 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.
590 $a School code: 0792.
650 4 $a Physics. $3 516296
650 4 $a Computational physics. $3 3343998
650 4 $a Materials science. $3 543314
650 4 $a Condensed matter physics. $3 3173567
653 $a Machine learning
653 $a Materials discovery
653 $a Neural networks
653 $a Solid state physics
690 $a 0605
690 $a 0794
690 $a 0216
690 $a 0611
710 2 $a State University of New York at Binghamton. $b Physics, Applied Physics, and Astronomy. $3 1266322
773 0 $t Dissertations Abstracts International $g 85-12B.
790 $a 0792
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31296909