LAB NOTICE
- [정찬혁/김상윤 박사과정/허성구 교수][J. Hazard. Mat./IF10,JCR10%] Transformer-SMILES학습기반 PFAS종류별 end points QSPR 예측 모델(AI QSPR Modeling of PFAS Endpoint by transformer-Driven SMILES-to-Molecular Descriptor Learning)
- [정찬혁/김상윤 박사과정/허성구 교수][J. Hazard. Mat./IF10,JCR10%] Transformer-SMILES학습기반 PFAS종류별 end points QSPR 예측 모델(AI QSPR Modeling of PFAS Endpoint by transformer-Driven SMILES-to-Molecular Descriptor Learning) Good news,ChanHyeok and SangYoun together with Prof. Heo (KNU)'s paper has been accepted from Journal of Hazardous Materials (IF 10.6, JCR TOP 10%: Environmental Science). 게재 승인되었습니다.PFAS물성치 예측용 트랜스포머기반 SMILES부터 분자설명자 학습 기반 QSPR 유해화학물질 물성치 예측 모델 및 해석 평가 특별히 PFAS 화학구조 설명가능성과 데이타 기반 모델을 통한 PFAS Endpoints 예측 및 설명에 유용한 AI화학구조 특화 유해화학물질 PFAS 2000개 이상의 물성치 예측 및 학습반도체 산업에세 많이 쓰이는 PFAS 특화 인체위해화학물질팀과 환경안전 부문에 유용Topic : Interpretable QSPR Modeling of PFAS Endpoints via Transformer-Driven SMILES-to-Molecular Descriptor Learning1st authors : ChanHyeok Jeong1, SangYoun KimMain corresponding author : Prof. SungKu Heo (Kangwon National University,Coauthors : TaeYong Woo, NaYeong Jeon, SeonJu Kim (KNU)ChanHyeok Jeong1, SangYoun Kim1, TaeYong Woo, NaYeong Jeon, SeonJu Kim, ChangKyoo Yoo*, and SungKu Heo**, Interpretable QSPR Modeling of PFAS Endpoints via Transformer-Driven SMILES-to-Molecular Descriptor Learning, Journal of Hazardous Materials((ISSN:0304-3894, SCIE,JCR TOP 10%: Environmental Science), (Ack5개: BK4 KHYSS장학:GSO-20260492; NRF(도약, 신진, BRL:2026-25471555, RS-2026-25494019, and RS-2026-25504128), 과학기술정통부 GPU(RQT-25-090112)
2026-10-11
PUBLICATIONS
- Sustainable energies and machine learning: an organized review of recent applications and challenges
- Pouya Ifaei, Morteza Nazari-Heris, Amir Saman Tayerani Charmchi, Somayeh Asadi, ChangKyoo Yoo*, Sustainable energies and machine learning: an organized review of recent applications and challenges, Energy (ISSN: 0360-5442, SCI, Top 5% journal – THERMODYNAMICS), 266(1), pp.126432 (2023.3) (Ack 3개: Brain Pool Program2019H1D3A1A02071051 & NRF중견2021R1A2C2007838 & Collabo R&D of SMEs and Startups in 2022.(Project No. S3301144)
- Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach
- Shahzeb Tariq+, Jorge Loy-Benitez+, KiJeon Nam, SangYoun Kim, MinJeong Kim*, ChangKyoo Yoo*, Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach, Tunnelling and underground space (ISSN: 0886-7798, SCIE, JCR Top 10%-ENGINEERING, CIVIL, DOI), Elsevier, 131(1), pp.104843 (Ack: 2021R1A2C2007838 & Fine Dust Reduction Technology(21QPPW-B152306-03)
- Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving
- Abdulrahman H. Ba-Alawi, KiJeon Nam, SungKu Heo, TaeYong Woo, Hanaa Aamer, and Chang Kyoo Yoo*, Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving, Chemical Engineering Journal (IF>16.4, JCR TOP 3% journal/Top 1 journal-Chemical Engineering), 452(1), pp.139220 (2023.1) (Ack 3개:NRF2021R1A2C2007838, “Prospective green technology innovation project (2020003160009), SMEs and Startups in 2022 (Project No. S3301144)
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