Machine Learning the Energy Storage Materials.
ORAL
Abstract
Data driven machine learning approaches have been increasingly adopted in
the materials science community as an efficient alternative to density functional
theory calculations to predict the properties of materials. In this work, we show
how machine learning can be applied to energy storage materials.
In addition, we compare the performance of different machine learning approaches
for predicting the future energy storage materials.
the materials science community as an efficient alternative to density functional
theory calculations to predict the properties of materials. In this work, we show
how machine learning can be applied to energy storage materials.
In addition, we compare the performance of different machine learning approaches
for predicting the future energy storage materials.
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Presenters
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Rajendra Joshi
Department of Physics, Central Michigan University, Department of Physics and Science of Advanced Materials, Central Michigan University, Mt. Pleasant, MI, 48858
Authors
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Rajendra Joshi
Department of Physics, Central Michigan University, Department of Physics and Science of Advanced Materials, Central Michigan University, Mt. Pleasant, MI, 48858
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Veronica Barone
Department of Physics and Science of Advanced Materials, Central Michigan University, Mt. Pleasant, MI, 48858
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Jesse Eickholt
Department of Computer Science, Central Michigan University, Mt. Pleasant, MI, 48858
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Juan Ernesto Peralta
Department of Physics and Science of Advanced Materials, Central Michigan University, Mt. Pleasant, MI, 48858