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://www.pnas.org/content/118/5/e2016917118). Practical guidance is given in (https://arxiv.org/abs/2106.07465).

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.

Custom layers and networks

logsLayer.m

Logsigmoidal activation function layer.

renormLayer.m

Renormalisation layer for probability distributions.

Service functions

DEERDatastore.m

Infinite datastore of DEER data for spin-1/2 electrons.

deerplot.m

Plotting subsystem of DEERNet.

dist_net.m

Returns an untrained distance distribution DEERNet for processing fully sampled data.

dist_vet.m

Returns an untrained distance distribution DEERNet for processing sparsely sampled data.

iridis_net.m

Trains one DEERNet on an Iridis5 supercomputer node at the University of Southampton.

iridis_vet.m

Trains one DEERVet on an Iridis5 supercomputer node at the University of Southampton.

library_dd.m

Training database parameters for the purely dipolar DEERNet.

quality_control.m

Internal heuristics designed to catch malformed inputs.

signal_model.m

DEER signal model used for DEERNet and DEERVet backcalculations.

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