| 학회 | 한국화학공학회 |
| 학술대회 | 2021년 봄 (04/21 ~ 04/23, 부산 BEXCO) |
| 권호 | 27권 1호, p.86 |
| 발표분야 | 신진연구자 심포지엄 |
| 제목 | Atomistic Modeling and Artificial Intelligence for Materials Discovery |
| 초록 | The discovery and development of the novel material take an average of 18 years to commercialize and requires the huge amount of manpower and resources [1]. The Edison-type trial-and-error method has limitations in the development of high-performance materials especially in the field of energy. First-principles calculations can provide in-depth understanding on the reaction mechanisms that can guide designing new materials [2,3]. High-throughput screening with first-principles calculation allow us to explore large materials space efficiently [4]. Also, it is expected that self-driving laboratories with artificial intelligence and robotics can accelerate the development of new materials. In this talk, I will present the first-principles results on the electrode materials for Li-ion batteries [2,3] and the mixed ionic and electronic conductors for solid-state Li-air batteries [4]. I will also briefly introduce our recent progress on the self-driving laboratories to discover new materials for Li-ion batteries. [1] Nature Mater., 12, 173 (2013) [2] Nature Chem., 8, 692 (2016) [3] Science, 367 (6481) 1030 (2020) [4] Adv. Energy Mater., 10(38) 2001767 (2020) |
| 저자 | 서동화 |
| 소속 | 울산과학기술원 |
| 키워드 | Li-ion batteries; Atomistic modeling; Artificial intelligence; Self-driving laboratories |
| 원문파일 | 초록 보기 |
