東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

The Discovery and Design of Rare-Ear...

Liao, Timothy,

Linked to FindBook

Google Book

Amazon

博客來

The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning // Timothy Liao.
Author:	Liao, Timothy,
Description:	1 electronic resource (175 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Electromagnetics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31690669
ISBN:	9798384443049

The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning /
Liao, Timothy,

The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning /Timothy Liao. - 1 electronic resource (175 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

In today's rapidly advancing technological landscape, the reliance on powerful magnets spans various sectors, including advanced technology and green energy. These magnets, integral to the functionality of devices like wind turbines, electric cars, and computer hard disks, often feature rare-earth elements such as neodymium or samarium. Given the supply risks associated with rare-earth elements, my dissertation research focuses on discovering strong magnets without them. I leverage efficient first principles calculations and machine learning to accelerate the typically slow trial-and-error method in experiments. This presentation outlines the efforts, culminating in the discovery of a new material, Fe3CoB2. Initially, I establish a magnetic materials database encompassing magnetization (i.e., magnetic strength), magnetic anisotropy (i.e., magnetic field resilience), and Curie temperature (i.e., heat resilience). Then, I enhance the functionality of a popular machine learning model to predict both macroscopic and microscopic properties, validated through high-throughput first-principles calculations on Fe-Co-N.Following successful machine learning model testing, I introduce an adaptive machine learning feedback system for efficient materials discovery. This framework streamlines the computational discovery and experimental synthesis of Fe3CoB2, reducing the process to mere days. Building on this achievement, I extend the search to Fe-Co-Si compounds, expected to be compatible with a Si substrate. Finally, I evaluate the performance of our top candidate Fe-Co-X (X = N, B, Si, P, and C) structures against common rare-earth and rare-earth-free magnets.

English

ISBN: 9798384443049Subjects--Topical Terms:

3173223
Electromagnetics.
Subjects--Index Terms:

Machine learning

The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning /
LDR:03252nmm a22004693i 4500 001 2429423
005 20250430100824.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798384443049
035 $a (MiAaPQD)AAI31690669
035 $a (MiAaPQD)123vireo24537Liao
035 $a AAI31690669
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Liao, Timothy, $e author. $3 3817265
245 1 0 $a The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning / $c Timothy Liao.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (175 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
500 $a Advisors: Chelikowsky, James R. Committee members: Demkov, Alexander A.; Giustino, Feliciano; Lai, Keji; Hwang, Gyeong S.
502 $b Ph.D. $c The University of Texas at Austin $d 2024.
520 $a In today's rapidly advancing technological landscape, the reliance on powerful magnets spans various sectors, including advanced technology and green energy. These magnets, integral to the functionality of devices like wind turbines, electric cars, and computer hard disks, often feature rare-earth elements such as neodymium or samarium. Given the supply risks associated with rare-earth elements, my dissertation research focuses on discovering strong magnets without them. I leverage efficient first principles calculations and machine learning to accelerate the typically slow trial-and-error method in experiments. This presentation outlines the efforts, culminating in the discovery of a new material, Fe3CoB2. Initially, I establish a magnetic materials database encompassing magnetization (i.e., magnetic strength), magnetic anisotropy (i.e., magnetic field resilience), and Curie temperature (i.e., heat resilience). Then, I enhance the functionality of a popular machine learning model to predict both macroscopic and microscopic properties, validated through high-throughput first-principles calculations on Fe-Co-N.Following successful machine learning model testing, I introduce an adaptive machine learning feedback system for efficient materials discovery. This framework streamlines the computational discovery and experimental synthesis of Fe3CoB2, reducing the process to mere days. Building on this achievement, I extend the search to Fe-Co-Si compounds, expected to be compatible with a Si substrate. Finally, I evaluate the performance of our top candidate Fe-Co-X (X = N, B, Si, P, and C) structures against common rare-earth and rare-earth-free magnets.
546 $a English
590 $a School code: 0227
650 4 $a Electromagnetics. $3 3173223
650 4 $a Materials science. $3 543314
650 4 $a Alternative energy. $3 3436775
650 4 $a Computational chemistry. $3 3350019
653 $a Machine learning
653 $a Green energy
653 $a Magnetic materials
653 $a Computational discovery
653 $a Wind turbines
690 $a 0794
690 $a 0800
690 $a 0607
690 $a 0219
690 $a 0363
710 2 $a The University of Texas at Austin. $b Physics. $e degree granting institution. $3 3817266
720 1 $a Chelikowsky, James R. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-04B.
790 $a 0227
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31690669