The PiNet network
The PiNet network implements the network architecture described in our paper.1 The network architecture features the graph-convolution which recursively generates atomic properties from local environment.
One distinctive feature of PiNet is that the convolution operation is realized with pairwise functions whose form are determined by the pair, called pairwise interactions.
Parameters
| Parameter | Default | Description |
|---|---|---|
| atom_types | [1, 6, 7, 8] |
List of elements |
| rc | 4.0 |
Cutoff radius |
| cutoff_type | 'f1' |
One of 'f1' or 'f2' |
| basis_type | 'polynomial' |
One of 'polynomial' or 'gaussian' |
| n_basis | 4 |
Number of radial basis functions to generate |
| gamma | 3.0 |
controls width of the Gaussian basis |
| center | None |
replace centers the for Gaussian basis with a list |
| pp_nodes | [16, 16] |
specifies the property-property hidden layers |
| pi_nodes | [16, 16] |
specifies the property-interaction hidden layers |
| ii_nodes | [16, 16] |
specifies the interaction-interaction hidden layers |
| out_nodes | [16, 16] |
specifies the output hidden layers |
| out_units | 1 |
the dimension of outputs |
| out_pool | False |
min, max or sum, pool atomic outputs to give global predictions |
| act | 'tanh' |
activation function to use |
| depth | 4 |
number of graph-convolution layers to use |
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Y. Shao, M. Hellström, P. D. Mitev, L. Knijff, and C. Zhang. PiNN: a python library for building atomic neural networks of molecules and materials. J. Chem. Inf. Model., 60:1184–1193, January 2020. doi:10.1021/acs.jcim.9b00994. ↩