基本信息
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个人简介
Theory, computational, electronic structure theory, machine learning, materials theory, photophysics, spectroscopy
Projects
MACHINE LEARNING FOR CHEMISTRY
Molecules are composed of functional groups that behave similarly in different contexts. We are using machine learning to develop quantum chemical methods that take better advantage of this molecular similarity. In particular, we are developing neural networks that use quantum chemistry as an integral part of their prediction process. By training the neural network on data from ab initio computations, the neural net learns to predict molecular properties at a cost that is a small fraction of that of ab initio theory. We are also developing ways to use machine learning to control experimental conditions and drive reactions to desired outcomes.
Projects
MACHINE LEARNING FOR CHEMISTRY
Molecules are composed of functional groups that behave similarly in different contexts. We are using machine learning to develop quantum chemical methods that take better advantage of this molecular similarity. In particular, we are developing neural networks that use quantum chemistry as an integral part of their prediction process. By training the neural network on data from ab initio computations, the neural net learns to predict molecular properties at a cost that is a small fraction of that of ab initio theory. We are also developing ways to use machine learning to control experimental conditions and drive reactions to desired outcomes.
研究兴趣
论文共 201 篇作者统计合作学者相似作者
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ENVIRONMENTAL SCIENCE & TECHNOLOGYno. 12 (2024): 5347-5356
JOURNAL OF CHEMICAL EDUCATIONno. 6 (2023): 2116-2131
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Journal of Chemical Educationno. 6 (2023): 2116-2131
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arxiv(2023)
Chemistry (Weinheim an der Bergstrasse, Germany)no. 38 (2023)
Emily C. King, Max Benson, Sandra Raysor,Thomas A. Holme,Jonathan Sewall,Kenneth R. Koedinger,Vincent Aleven,David J. Yaron
crossref(2021)
Simulations and Student Learningpp.107-115, (2021)
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