1D convolutional neural net for predicting the lamellar period of copolymers based on sequence of beads.
Members: Ruijie Zhu, Kastan Day, Aria Coraor, Seonghwan Kim, Jiahui Yang
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├── data # input data file for feature generation
├── features # folder containing scripts used to generate features and all features used to train the neural net
├── Multi-channel PolyConvNet.ipynb # code used to train / test the Multi-channel PolyConvNet
├── Multi-channel PolyConvNet VAE.ipynb # code used to train / test the Multi-channel PolyConvNet with VAE features
├── models # folder containing all trained models
├── LICENSE
└── README.md
1. Sliding window features
29-dimensional feature used to capture the activation of polymer sequence
2. Kernels
- Exponential kernel: 30-dimensional feature used to capture the interaction at two ends
- Cosine kernel: 15-dimensional feature used to capture the periodicity of sequence
3. VAE features
4-dimensional feature generated using the Variational Autoencoder model
4. Interaction parameter
The model consists of a series of convolution layer and fully connected layers that extract patterns from the polymer sequence.
Feature Generation | Time (min) |
---|---|
Sliding Window Features (4 channels) | 0.5 |
Kernel Features | 0.08 |
VAE Features | 30 |
Model Training/Validation | Time (min) |
---|---|
Training | 1 |
Validation | 0.02 |
- All runtimes reported using Theta GPU