DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김지한 | - |
dc.contributor.author | Park, Yoonseo | - |
dc.contributor.author | 박윤서 | - |
dc.date.accessioned | 2024-07-30T19:31:05Z | - |
dc.date.available | 2024-07-30T19:31:05Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096703&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321486 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 생명화학공학과, 2024.2,[iii, 27 p. :] | - |
dc.description.abstract | As interest in Metal-Organic Frameworks (MOFs) increases, so does the need for research into their thermal stability. This study focused on predicting the thermal stability of MOFs based on various characteristics derived from their structure. We employed two main approaches: using the reactive force field (ReaxFF) to screen the thermal stability of multiple MOFs simultaneously and developing a machine learning model for large datasets to predict thermal stability and investigate the factors involved in it. Notably, the classification model developed in the second approach not only demonstrated high accuracy but also provided insights into the factors affecting the thermal stability of MOFs. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Metal-organic frameworks▼aThermal stability▼aMachine learning▼aMolecular dynamics▼aReactive force field | - |
dc.subject | 금속 유기 골격체▼a열안정성▼a기계학습▼a분자동역학▼aReactive force field | - |
dc.title | Machine learning-based classification of thermal stability in metal-organic frameworks | - |
dc.title.alternative | 금속-유기 구조체의 열 안정성에 대한 기계 학습 기반 분류 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :생명화학공학과, | - |
dc.contributor.alternativeauthor | Kim, Ji Han | - |
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