| 12212 | M(η5-C5Me5)TEA [M=Ti, Zr, Hf] 메탈로센촉매와 공촉매인 Methylaluminoxane에 의한 에틸렌 중합 Son Ki Ihm, , 1999-01-01 |
| 12213 | Machine learning applications in genome-scale metabolic modeling Kim, Yeji; Kim, Gi Bae; Lee, Sang Yup, Current Opinion in Systems Biology, v.25, pp.42 - 49, 2021-03 |
| 12214 | Machine learning applications in systems metabolic engineering Kim, Gi Bae; Kim, Won Jun; Kim, Hyun Uk; Lee, Sang Yup, CURRENT OPINION IN BIOTECHNOLOGY, v.64, pp.1 - 9, 2020-08 |
| 12215 | Machine learning based epoxy resins properties prediction = 머신러닝을 활용한 에폭시수지 물성 예측link Jang, Huiwon; Kim, Jihan; et al, 한국과학기술원, 2022 |
| 12216 | Machine learning for heterogeneous catalysts and their synthesizability Jung, Yousung, ACS Fall Meeting, American Chemical Society, 2021-08-23 |
| 12217 | Machine learning of activation energy prediction for extended element = 기계 학습을 통한 확장된 원소에 대한 활성화 에너지 예측link Park, Jongseo; Jung, Yousung; et al, 한국과학기술원, 2022 |
| 12218 | Machine learning to explore solid-state chemical space Jung, Yousung, Toward Inverse Design of Functional Inorganic Materials, Materials Research and Engineering (IMRE), 2020-01-28 |
| 12219 | Machine learning-based classification of thermal stability in metal-organic frameworks = 금속-유기 구조체의 열 안정성에 대한 기계 학습 기반 분류link Park, Yoonseo; 박윤서; et al, 한국과학기술원, 2024 |
| 12220 | Machine learning-based discovery of molecules, crystals, and composites: A perspective review Lee, Sangwon; Byun, Haeun; Cheon, Mujin; Kim, Jihan; Lee, Jay Hyung, KOREAN JOURNAL OF CHEMICAL ENGINEERING, v.38, no.10, pp.1971 - 1982, 2021-10 |
| 12221 | Machine learning-based evaluation of model extraction and simulation methods for high-quality cancer patient-specific metabolic models 이상미; 이가령; 김현욱, BIOINFO 2022, 한국생명정보학회, 2022-10-21 |
| 12222 | Machine learning-enhanced prediction of optimal metal-organic frameworks for O$_2$/N$_2$ gas separation = O$_2$/N$_2$ 가스 분리를 위한 최적의 금속-유기 구조체에 대한 기계 학습 예측link Kum, Hwayeon; 금화연; et al, 한국과학기술원, 2024 |
| 12223 | Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models Lee, Sang Mi; Lee, GaRyoung; Kim, Hyun Uk, COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, v.20, pp.3041 - 3052, 2022-06 |
| 12224 | Machine learning: Overview of the recent progresses and implications for the process systems engineering field Lee, Jay Hyung; Shin, Joohyun; Realff, Matthew J., COMPUTERS & CHEMICAL ENGINEERING, v.114, pp.111 - 121, 2018-06 |
| 12225 | Machine-Accelerated Materials Structure-Property-Synthesizability Prediction Jung, Yousung, The International Chemical Congress of Pacific Basin Societies 2021, Pacifichem, 2021-12-20 |
| 12226 | Machine-Enabled Chemical Structure-Property-Synthesizability Predictions 정유성, 제 24회 2022 고분자 신기술 강좌, 한국고분자학회, 2022-10-05 |
| 12227 | Machine-Enabled Exploration of Materials Chemical Space 정유성, 2020년 한국세라믹학회 춘계학술대회, 한국세라믹학회, 2020-07-06 |
| 12228 | Machine-Enabled Exploration of Materials Space 정유성, 2020년도 대한금속 재료학회 추계학술대회(제9회 뉴호라이즌 심포지엄(인공지능재료과학분과), 대한금속재료학회, 2020-10-29 |
| 12229 | Machine-enabled inverse design of inorganic solid materials: promises and challenges Noh, Juhwan; Gu, Geun Ho; Kim, Sungwon; Jung, Yousung, CHEMICAL SCIENCE, v.11, no.19, pp.4871 - 4881, 2020-05 |
| 12230 | Machine-enabled inverse design of solid-state materials Jung, Yousung, NANO KOREA 2020, NANO KOREA Symposium, 2020-07-02 |
| 12231 | Machine-Learning Based Porous Materials Design 김지한, 한국세라믹학회 추계학술대회, 한국세라믹학회, 2020-11-24 |
