Neural network module

DEERNet is a collection of functions that make and use deep neural networks for processing DEER data. The approach is described in detail in (https://dx.doi.org/10.1126/sciadv.aat5218). The reasons why it works so well are analysed in (https://arxiv.org/abs/1912.01498).

Using DEERNet

Import your data with elexsys2deernet.m and feed it into deernet.m - examples are provided in examples/deernet directory. You will get outputs that look like the following:

Functions - DEERNet

deer_lib_gen.m

Generates a library of simulated DEER data for use in neural network training and validation.

deernet.m

Uses an ensemble of neural networks to extract distance distributions from DEER data.

dist_range.m

Distance range estimation for a given time grid.

elexsys2deernet.m

Prepares standard Bruker Elexsys datasets for input into the deernet.m function.

process_using.m

Runs DEER data processing using a specified neural network file.

train_one_net.m

Trains a single neural network using supplied parameters.

Functions - descrambling

descramble.m

Weight matrix descrambling using Tikhonov smoothness criterion.

left_diag.m

Weight matrix descrambling using maximum diagonality criterion.

Service functions

quality_control.m

Internal heuristics designed to catch malformed inputs.

Version 2.6, authors: Ilya Kuprov, Steve Worswick, Jake Amey, Jake Keeley, Tajwar Choudhury