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Yuan Qi, Hong Ren, Hong Li, Dinglin Zhang, Hongqiang Cui, Junben Weng, Guohui Li, Guiyan Wang, Yan Li. Interaction energy prediction of organic molecules using Deep Tensor Neural Network[J]. Chinese Journal of Chemical Physics .
Citation: Yuan Qi, Hong Ren, Hong Li, Dinglin Zhang, Hongqiang Cui, Junben Weng, Guohui Li, Guiyan Wang, Yan Li. Interaction energy prediction of organic molecules using Deep Tensor Neural Network[J]. Chinese Journal of Chemical Physics .

Interaction energy prediction of organic molecules using Deep Tensor Neural Network

  • Received Date: 2020-09-09
  • Accepted Date: 2020-10-08
  • Rev Recd Date: 2020-09-26
  • Available Online: 2020-11-11
  • The interaction energy of two molecules system plays a critical role in analyzing the interacting effect in Molecular Dynamic simulation. Since the limitation of quantum mechanics calculating resources, the interaction energy based on quantum mechanics can not be merged into Molecular Dynamic simulation for a long time scale. A deep learning framework, Deep Tensor Neural Network, is applied to predict the interaction energy of three organic related systems within the quantum mechanics level of accuracy. The geometric structure and atomic types of molecular conformation, as the data descriptors, are applied as the network inputs to predict the interaction energy in the system. The neural network is trained with the hierarchically generated conformations data set. The complex tensor hidden layers are simplified and trained in the optimization process. The predicted results of different molecular systems indicate that Deep Tensor Neural Network is capable to predict the interaction energy with 1 KCal/mol of the mean absolute error in a relatively short time. The prediction highly improves the efficiency of interaction energy calculation. The whole proposed framework provides new insights to introduce deep learning technology into the interaction energy calculation.
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通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Interaction energy prediction of organic molecules using Deep Tensor Neural Network

Abstract: The interaction energy of two molecules system plays a critical role in analyzing the interacting effect in Molecular Dynamic simulation. Since the limitation of quantum mechanics calculating resources, the interaction energy based on quantum mechanics can not be merged into Molecular Dynamic simulation for a long time scale. A deep learning framework, Deep Tensor Neural Network, is applied to predict the interaction energy of three organic related systems within the quantum mechanics level of accuracy. The geometric structure and atomic types of molecular conformation, as the data descriptors, are applied as the network inputs to predict the interaction energy in the system. The neural network is trained with the hierarchically generated conformations data set. The complex tensor hidden layers are simplified and trained in the optimization process. The predicted results of different molecular systems indicate that Deep Tensor Neural Network is capable to predict the interaction energy with 1 KCal/mol of the mean absolute error in a relatively short time. The prediction highly improves the efficiency of interaction energy calculation. The whole proposed framework provides new insights to introduce deep learning technology into the interaction energy calculation.

Yuan Qi, Hong Ren, Hong Li, Dinglin Zhang, Hongqiang Cui, Junben Weng, Guohui Li, Guiyan Wang, Yan Li. Interaction energy prediction of organic molecules using Deep Tensor Neural Network[J]. Chinese Journal of Chemical Physics .
Citation: Yuan Qi, Hong Ren, Hong Li, Dinglin Zhang, Hongqiang Cui, Junben Weng, Guohui Li, Guiyan Wang, Yan Li. Interaction energy prediction of organic molecules using Deep Tensor Neural Network[J]. Chinese Journal of Chemical Physics .

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