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

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dc.contributor.advisorJung, You Sung-
dc.contributor.advisor정유성-
dc.contributor.authorKim, Sungwon-
dc.date.accessioned2021-05-11T19:32:15Z-
dc.date.available2021-05-11T19:32:15Z-
dc.date.issued2019-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=875274&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/282980-
dc.description학위논문(석사) - 한국과학기술원 : 생명화학공학과, 2019.8,[iii, 20 p. :]-
dc.description.abstractFor 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.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectinorganic material▼aDFT▼amachine learning▼agenerative model▼apoint cloud-
dc.subject무기 재료▼aDFT 계산▼a기계학습▼a생성 모델▼a점 구름-
dc.titleInverse design for inorganic solid materials using generative adversarial neural network = 적대 생성 신경망을 이용한 고체 무기재료의 역설계-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :생명화학공학과,-
dc.contributor.alternativeauthor김성원-
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