DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Woo Youn | - |
dc.contributor.advisor | 김우연 | - |
dc.contributor.author | Kim, Jin Woo | - |
dc.date.accessioned | 2022-04-21T19:34:51Z | - |
dc.date.available | 2022-04-21T19:34:51Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=956535&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/295795 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 화학과, 2021.2,[v, 72 p. :] | - |
dc.description.abstract | Despite remarkable advances in computational chemistry, prediction of reaction mechanism is still challenging, because investigating all possible reaction pathways is computationally prohibitive due to the high complexity of chemical space. In this thesis, we introduce the novel approach of automated chemical reaction prediction program named ACE-Reaction. ACE-Reaction constructs reaction network and extract the minimal network composed of only favorable reaction pathways from the complex chemical space. This method has been applied to various chemical reactions and verified its reliability and broad applicability. In addition, machine learning techniques and Monte Carlo tree search method are employed for chemical reactivity learning. It enables more efficient chemical reaction space exploration. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Automatic reaction prediction method▼aReaction network▼aCheminformatics▼aMachine learning | - |
dc.subject | 자동화된 화학 반응 예측 방법▼a반응 네트워크▼a화학 정보학▼a기계 학습 | - |
dc.title | Reaction mechanism prediction via automated reaction network analysis and chemical reactivity learning | - |
dc.title.alternative | 자동화된 반응 네트워크 분석과 반응성 학습을 통한 반응 메커니즘 예측 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :화학과, | - |
dc.contributor.alternativeauthor | 김진우 | - |
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