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학술발표회초록보기

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  • 09월 01일 18시 이후 : 초록수정 불가능, 일정확인 및 검색만 가능

제118회 대한화학회 학술발표회, 총회 및 기기전시회 안내 Machine learning approaches to the configuration energies and chemisorption models in solids

등록일
2016년 9월 2일 15시 32분 26초
접수번호
2509
발표코드
PHYS1-4 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
목 14시 : 40분
발표형식
심포지엄
발표분야
물리화학 - Machine Learning and Chemistry
저자 및
공동저자
정유성
KAIST EEWS 대학원, Korea
Machine learning approaches are now beginning to be actively explored in theoretical chemistry, and in particular, perhaps as an important starting point, efficiently describing the energies of molecules have recently been proposed and shown reasonable performances. Yet, the need for the machine to eventually learn many-body effects as well as subtle correlations poses a significant challenge for these approaches to achieve high accuracy for general purposes. In this talk, we focus on the configuration energies of solids and some chemisorption models on them, and illustrate that the machine learning approaches can indeed be a useful alternative to the existing models to evaluate the latter energies. For both cases, we obtain mean absolute errors of 0.05-0.13 eV based on the artificial neural networks.

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