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The Discovery and Design of Rare-Ear...

Liao, Timothy,

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning // Timothy Liao.
作者:	Liao, Timothy,
面頁冊數:	1 electronic resource (175 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
標題:	Electromagnetics. -
電子資源:	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 /
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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.
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