Journal of Computer Chemistry, Japan
Online ISSN : 1347-3824
Print ISSN : 1347-1767
ISSN-L : 1347-1767

3次元畳み込みニューラルネットワークの転移学習を用いたブロック共重合体の応力ひずみ曲線予測

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2021 年 20 巻 3 号 p. 100-102

The simulation to obtain stress-strain curves of block copolymers requires large computational resources. As an alternative to simulation, a method for high-throughput prediction of stress-strain curves using a three-dimensional convolutional neural network has been reported. In this study, we incorporated shortcut coupling into the neural network and performed pre-training and transfer learning in a step-by-step manner to successfully predict the stress-strain curve with high accuracy while reducing the training cost.