Computational generation of porous material structures for neural net based discovery인공신경망 기반의 다공성 물질 개발을 위한 구조 생성 알고리즘
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Kim, Jihan | - |
| dc.contributor.advisor | 김지한 | - |
| dc.contributor.author | Kim, Baekjun | - |
| dc.date.accessioned | 2019-09-03T02:43:17Z | - |
| dc.date.available | 2019-09-03T02:43:17Z | - |
| dc.date.issued | 2018 | - |
| dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=733878&flag=dissertation | en_US |
| dc.identifier.uri | http://hdl.handle.net/10203/266310 | - |
| dc.description | 학위논문(석사) - 한국과학기술원 : 생명화학공학과, 2018.2,[iii, 23 p. :] | - |
| dc.description.abstract | We present a novel computational approach using the artificial neural networks (ANNs) that can generate the hypothetical adsorption properties. For the learning of ANNs, the molecular simulation screened more than 330,000 zeolite structures. In addition, we developed the structure prediction algorithm that is working on the energy grid space. We reproduced the eleven zeolite structures when the experimentally synthesized structures are used as the input. | - |
| dc.language | eng | - |
| dc.publisher | 한국과학기술원 | - |
| dc.subject | Material discovery▼aZeolite▼aArtificial neural networks▼aMolecular simulation▼aGas adsorption | - |
| dc.subject | 물질 개발▼a제올라이트▼a인공신경망▼a분자 시뮬레이션▼a가스 흡착 | - |
| dc.title | Computational generation of porous material structures for neural net based discovery | - |
| dc.title.alternative | 인공신경망 기반의 다공성 물질 개발을 위한 구조 생성 알고리즘 | - |
| dc.type | Thesis(Master) | - |
| dc.identifier.CNRN | 325007 | - |
| dc.description.department | 한국과학기술원 :생명화학공학과, | - |
| dc.contributor.alternativeauthor | 김백준 | - |
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