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Materials Algorithms Project
Program Library


  1. Provenance of code.
  2. Purpose of code.
  3. Specification.
  4. Description of program's operation.
  5. References.
  6. Parameter descriptions.
  7. Error indicators.
  8. Accuracy estimate.
  9. Any additional information.
  10. Example of code
  11. Auxiliary routines required.
  12. Keywords.
  13. Download source code.
  14. Links.

Provenance of Source Code

Richard Darby
Department of Materials Science and Metallurgy,
Pembroke Street,
University of Cambridge,
Cambridge, CB2 3QZ.

The neural network program was produced by:
David MacKay,
Cavendish Laboratory,
University of Cambridge,
Madingley Road,
Cambridge, CB3 0HE, U.K.

E-mail: Richard Darby

Added to MAP: May 2005

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Neural Network model of data for lattice parameter of delta plutonium.

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Language: C & Fortran
Product form: Windows Executable and Unix source for compilation.

Complete program.

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The modelling procedure is based on a neural network program called generate44, which was developed by David MacKay and is part of the bigback5 program. The model is constituted of a committee of several individual neural networks. It was trained on a set of experimental data for which the "outputs" are known, and creates a kind of non-linear, multi-parameter "regression" of the outputs versus the inputs. This "regression" has already been produced and the model is delivered ready to perform predictions for steels of any desired composition (within certain specified limits). The source code for the neural network program can be downloaded from David MacKay's website; the executable files only are available from MAP.

The program runs on a unix like operating system, Sun, Linux and Irix and on Windows systems which have DOS. The files for unix are separated compressed into a file called MAP_NN_LP_DPU.tar.gz the files for the windows systems are in the zip file MAP_NN_LP_DPU.zip  ;The achive file contains the following files which make the model:

A brief file with instructions for running the program.
A list of the input variables.
A text file containing the minimum and maximum limits of each input and output variable. This file is used to normalise and unnormalise the input and output data.
An input text file containing the input variables used for predictions, together with an example set of data.
Contains the results you should expect from the example set of data. To test the model is running properly on your computer, use the given 'test.dat' file to do predictions and compare the 'result' file with this file.
This is a unix shell file containing the command steps required to run the module. It can be executed by typing sh model.gen  at the command prompt. These shell files run all the programs necessary for normalising the input data, executing the network for each model, unnormalising the output data and combining the results of each model to produce the final committee result.
Created by generate_spec, which contains information about the module and the number of data items being supplied. It is read by the program generate44.
.generate_spec (hidden)
This executable file creates a file called spec.t1, required by generate44.
.randomise (hidden)
This executable file creates a file called norm_test.in, which contains the normalised equivalent of the input data found in test.dat. It requires the MINMAX file
This is the executable file for the neural network program. It reads the normalised input data file, norm_test.in (created by normalise) , and uses the weight files in subdirectory c, to find a value for the output. The results are written to the temporary output file _out.
This executable file combines the predictions of the different models in the committee and calculates the combined error bar.
This executable un-normalise the committee predictions and produces the file 'result'.
Contains the final un-normalised committee results for the predicted output.
The weights files for the different models.
Files containing information for calculating the size of the error bars for the different models.
Files containing information about the perceived significance value for each model.
Files containing values for the noise, test error and log predictive error for each model.
A normalised output file which was created during the building of the model. It is accessed by generate44 via spec.t1.
The normalised output file containing the committee results. It is generated by .gencom.

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  1. W. J. Pearson, 1967, Handbook of lattice spacings and structures of metals, 587-591.
  2. F. H. Ellinger, 1956, Journal of Metals, 8, 1256-1259.
  3. Richard Darby, Project report.

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Input parameters

The inputs to this model along with their labels in the program and units to be used:

Aluminium Concentration - Al(%)
at. %.

Temperature - Temp(C)

Quasichemical Solution Model Parameter - Quasi

Invar Model Parameter - Invar

Output parameters

The output of this model is the lattice parameter prediction with error bars that depend upon the position in the input space.

Lattice Parameter - a(A)

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Error Indicators


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Each prediction is accompanied with an estimated error that depends upon the position in the input space.

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Further Comments

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1. Download the model

Download and uncompress the appropriate archive file in a dedicated directory (for example: "neural").
On UNIX systems, this is done by:

On Microsoft systems you will first need to have the unzip or winzip programs installed.

2. Running the program (making predictions)

There are brief instructions in the README file inside each archive file. The unix download first needs to be compiled before you make predictions, this is done by the command:
sh install
Predictions are then made from the test.dat file using the command:
sh model.gen

On Windows systems the model is run by the command:

3. Program results

The results are written in the "Result" or "model_result.dat" file, as described in the README file.

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Auxiliary Routines

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neural networks, delta, plutonium, aluminium, lattice parameter.

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Download source code for Unix

Download Windows Executable

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MAP originated from a joint project of the National Physical Laboratory and the University of Cambridge.

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