| 세션명 | Graduate Student Oral Session (I) (English) |
|---|---|
| 발표장 | 제9회장 |
| 논문코드 | 1O9-14 |
| 발표일 | 2021-10-21 |
| 발표시간 | 17:25-17:40 |
| 논문제목 | A deep learning-based defect detection and inverse design of block copolymer system |
| 발표자 | 안지훈 |
| 발표자 소속 | 전남대학교 |
| 저자 | 안지훈, Vikram Thapar, 허수미† |
| 소속 | 전남대학교 |
| 논문초록 | Recently, as an emerging paradigm of material science, deep learning has shown its potential in various research fields; there have been attempts to apply deep learning in designing molecules’ structures, analyzing spectral data, and even sampling more accurate free energy landscapes. Among this unlimited potential of deep learning technology, image processing using deep learning has been particularly outstanding. Here, we propose deep learning algorithms to identify defects and quantify the ordering qualities in lamella- and cylinder-forming block copolymer films, replacing conventional defect inspection tools involved with either manual defect detections or inefficient image processing steps. We further use the developed deep learning neural networks for an inverse design of system parameters to achieve a targeting self-assembled structure. |
THE POLYMER SOCIETY OF KOREA