Leveraging machine learning in the innovation of functional materials

Zhehao Sun; Hang Yin; Zongyou Yin

doi:10.1016/j.matt.2023.06.014

Leveraging machine learning in the innovation of functional materials

Zhehao Sun, Hang Yin, Zongyou Yin^*

^*Corresponding author for this work

Research School of Chemistry

Research output: Contribution to journal › Comment/debate › peer-review

6 Citations (Scopus)

Abstract

The development of novel functional materials holds enormous potential in various fields. This preview highlights the application of theoretical methods and machine learning techniques in a recent issue of Patterns, focusing on the exploration of two-dimensional doped tellurene and the efficient screening of suitable candidates for fin field-effect transistors.

Original language	English
Pages (from-to)	2553-2555
Number of pages	3
Journal	Matter
Volume	6
Issue number	8
DOIs	https://doi.org/10.1016/j.matt.2023.06.014
Publication status	Published - 2 Aug 2023

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10.1016/j.matt.2023.06.014

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title = "Leveraging machine learning in the innovation of functional materials",

abstract = "The development of novel functional materials holds enormous potential in various fields. This preview highlights the application of theoretical methods and machine learning techniques in a recent issue of Patterns, focusing on the exploration of two-dimensional doped tellurene and the efficient screening of suitable candidates for fin field-effect transistors.",

author = "Zhehao Sun and Hang Yin and Zongyou Yin",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier Inc.",

year = "2023",

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doi = "10.1016/j.matt.2023.06.014",

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T1 - Leveraging machine learning in the innovation of functional materials

AU - Sun, Zhehao

AU - Yin, Hang

AU - Yin, Zongyou

PY - 2023/8/2

Y1 - 2023/8/2

N2 - The development of novel functional materials holds enormous potential in various fields. This preview highlights the application of theoretical methods and machine learning techniques in a recent issue of Patterns, focusing on the exploration of two-dimensional doped tellurene and the efficient screening of suitable candidates for fin field-effect transistors.

AB - The development of novel functional materials holds enormous potential in various fields. This preview highlights the application of theoretical methods and machine learning techniques in a recent issue of Patterns, focusing on the exploration of two-dimensional doped tellurene and the efficient screening of suitable candidates for fin field-effect transistors.

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U2 - 10.1016/j.matt.2023.06.014

DO - 10.1016/j.matt.2023.06.014

M3 - Comment/debate

AN - SCOPUS:85166111714

SN - 2590-2393

VL - 6

SP - 2553

EP - 2555

JO - Matter

JF - Matter

IS - 8

ER -

Leveraging machine learning in the innovation of functional materials

Abstract

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