Inverse design for inorganic solid materials using generative adversarial neural network = 적대 생성 신경망을 이용한 고체 무기재료의 역설계

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For decades, ab-initio first principle quantum calculation has contributed to discovering new materials, and due to recent drastic advance in computing calculation power high throughput screening that calculates enormous candidate material data and discovers good functional materials among them is available. This material discovery method, however, relies on chemical intuition and experience and also can discover materials only limited in existing database. In this work, we propose the new material design framework using Generative Adversarial Network (GAN) in machine learning that addresses previous material design method. Also, we developed the inorganic solid material representation that is simply invertible and applied to vanadium oxide system with GAN model. We found entirely new polymorphs for vanadium oxide. 23 structures among them are thermodynamically stable and could be considered synthesizable.
Advisors
Jung, You Sungresearcher정유성researcher
Description
한국과학기술원 :생명화학공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 생명화학공학과, 2019.8,[iii, 20 p. :]

Keywords

inorganic material▼aDFT▼amachine learning▼agenerative model▼apoint cloud; 무기 재료▼aDFT 계산▼a기계학습▼a생성 모델▼a점 구름

URI
http://hdl.handle.net/10203/282980
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=875274&flag=dissertation
Appears in Collection
CBE-Theses_Master(석사논문)
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