| 학회 |
한국화학공학회 |
| 학술대회 |
2019년 가을 (10/23 ~ 10/25, 대전컨벤션센터) |
| 권호 |
25권 2호, p.2192 |
| 발표분야 |
Application Studies of Multiscale Molecular Modeling and Simulation in Sustainable Chemistry and Eng |
| 제목 |
기계학습을 통한 고체화학 물질공간 탐색 Exploring solid-state chemical space by machine learning |
| 초록 |
Discovery of a new material with desired properties is the ultimate goal of materials research. To date, a generally successful strategy has been to use chemical intuition and empirical rules to design new materials, but these conventional approaches require a significant amount of time and cost due to almost unlimited combinatorial possibilities of inorganic materials to explore in chemical space. A promising way to significantly accelerate the latter process is to incorporate all available knowledge and data to plan the synthesis of the next material. In this talk, I will present a few initial frameworks we have developed along this line to perform machine-learned density functional calculations, to predict the properties of a material using simple representations, and to generate new materials for a target property using materials deep generative model. |
| 저자 |
정유성1, 노주환2, 구근호1, 김성원2, 임주형1, 김주환2
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| 소속 |
1Department of Chemical and Biomolecular Engineering, 2KAIST |
| 키워드 |
기계학습; 소재역설계; 생성모델; 무기소재; 물성예측; DFT계산
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| E-Mail |
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| 원문파일 |
초록 보기 |
