| 학회 | 한국공업화학회 |
| 학술대회 | 2021년 가을 (11/03 ~ 11/05, 대구 엑스코(EXCO)) |
| 권호 | 25권 2호 |
| 발표분야 | 포스터-화학공정 |
| 제목 | AI assisted optimization of multicomponent OER catalysts composition. |
| 초록 | Anion exchange membrane (AEM) water electrolysis has been considered remarkable development of a hydrogen production method. Especially, Non-platinum-group metal (non-PGM) catalysts is efficient substitute with low-cost for the large scale industrial applications. To improve the slow reaction of OER in alkaline solution, efficient electrocatalysts is important. The stable and active electrocatalysts for OER in AEM water electrolyzer has been found that they contain transition metal ions. In this point, we built a gaussian process regression and bayesian optimization model for finding optimal composition ratio of improved catalysts using 5 transition metal ions (Fe, Co, Ni, Mn, Ce). We can ultimately obtain 50 iterations through the model. As a result, we identify the interrelation between component of transition metal ions and the performance of them by using data correlation analysis. These result using data-driven modelling may provide the insight to improve performance of non-PGM. |
| 저자 | 홍지수 1 , 이기봉 2 , 최창혁 3 , 이웅 1 |
| 소속 | 1 한국과학기술(연), 2 고려대, 3 광주과학기술(연) |
| 키워드 | AI ; water electrolysis ; OER ; AEM ; electrocatalyst ; gaussian process ; bayesian optimization |
