래들교환형 이강종 연연주 해석

Thesis (doctoral)--서울대학교 대학원 :재료공학부,2003.Docto

용탕충진거동 해석용 프로그램 개발

학위논문(석사)--서울대학교 대학원 :금속공학과 제련공학전공,1997.Maste

Web을 활용한 HTML 학습의 설계 및 구현

학위논문(석사)--아주대학교 교육대학원 :컴퓨터교육전공,2001Maste

Towards Organic Photodetectors Functioning Under Strong Sunlight. Machine-learning-assisted Design of Diarylethene n-type Dopants to Mix with p-type Organic Semiconductor P3HT

Linear dynamic range of organic photodetectors, which is typically narrow due to low mobility of organic semiconductor, has been extended by diarylethene (DAE) photochromic switches doped to a poly-3-hexylthiophene photoactive layer. A speculated mechanism is that DAE acts as n-type electron traps only in its aromatic closed form, which is predominant only under strong sunlight, addressing the early saturation problem on sunny days. We herein identified two optimal DAE derivatives out of ~100 candidates, using the TDDFT calculations on the HOMO-LUMO energies of their open-closed isomers (~400 data). Since this is only a small subset of ~105 candidates, we predicted the HOMO-LUMO energies of the remaining candidates by machine learning with various artificial neural network models and molecule representation methods. We were able to identify additional optimal candidates, which were screened by machine learning prediction, and were confirmed by TDDFT calculations

Organic Photodetectors Operating under Strong Sunlight: Combining Machine-Learning and Time-Dependent Density Functional Theory for Molecular Design of Diarylethene-Type Photochromic n-Type Dopants Mixed with p-Type Organic Semiconductors

Linear dynamic ranges (LDR) of organic photodetectors, which are typically narrow due to the low charge mobilities of organic semiconductors, have been extended by doping them with diarylethene (DAE) photochromic switches. The speculated mechanism is that DAE acts as n-type trap only in its aromatic closed form which is predominant under strong sunlight. This mechanism involves photo-switchable change transfer between p-type donor polymer and DAE. We thus herein design optimal DAE dopants according to a set of design rules embracing the roles of their HOMO/LUMO energies. We first identified two optimal dopants out of 133 candidates, using time-dependent density functional theory (TDDFT) to calculate the HOMO/LUMO energies of their open/closed isomers (532 data). We then predicted those of 40,000 candidates through machine learning, identified additional optimal dopants, and confirmed them with TDDFT. One of the designed dopants indeed succeeded in LDR extension in real experiments