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.

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.

quality_control.m

Internal heuristics designed to catch malformed inputs.

renormLayer.m

Renormalisation layer for probability distributions.

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