Materials Algorithms Project
Program Library
Program MAP_NEURAL_LATTICE_PARAMETER_AUSTENITE
- Provenance of code.
- Purpose of code.
- Specification.
- Description of program's operation.
- References.
- Parameter descriptions.
- Error indicators.
- Accuracy estimate.
- Any additional information.
- Example of code
- Auxiliary routines required.
- Keywords.
- Download source code.
- Links.
Mathew Peet
Phase Transformations and Complex Properties Group,
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: Mathew Peet
Added to MAP:December 2002.
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Neural Network model of data for lattice parameter of austenite.
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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 a purely empirical one, and 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_AUS.tar.gz the files for the windows systems are in the zip file MAP_NN_LP_AUS.zip
;The archieve file contains the following files which make the model:
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README
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A brief file with instructions for running the program.
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labels.txt
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A list of the input variables.
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MINMAX
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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.
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test.dat
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An input text file containing the input variables used for predictions, together with an example set of data.
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result_test.txt
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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.
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model.gen
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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.
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spec.t1
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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.
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.generate_spec (hidden)
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This executable file creates a file called spec.t1, required by
generate44.
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.randomise (hidden)
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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
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.generate44
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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.
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.gencom
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This executable file combines the predictions of the different models in the committee
and calculates the combined error bar.
.treatout
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This executable un-normalise the committee predictions and produces the file 'result'.
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result
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Contains the final un-normalised committee results for the predicted output.
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SUBDIRECTORY c
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_w*f
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The weights files for the different models.
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*.lu
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Files containing information for calculating the size of the error bars
for the different models.
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_c*
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Files containing information about the perceived significance value for each model.
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_R*
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Files containing values for the noise, test error and log predictive error
for each model.
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SUBDIRECTORY d
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outran.x
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A normalised output file which was created during the building of the model.
It is accessed by generate44 via spec.t1.
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SUBDIRECTORY outprdt
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com.dat
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The normalised output file containing the committee results. It is generated by .gencom.
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- W. J. Pearson, 1967, Handbook of lattice spacings and structures of metals, 2, .
- D. J. Dyson and B. Holmes, 1970, Journal of Iron and Steel Institute, may, 469.
- N. Ridley and H. Stuart, 1970, Metal Science Journal, 4, 219.
- M. Onink, C.M. Brakman, J. H. Root, 1993, Scripta Metall et. Materialia, 29-8, 1011-1016.
- Cockett and Davis, 1963, Journal of Iron and Steel Institute, , .
- A. T. Gorton, G. Bitsianes, T.L. Joseph, 1965, Trans. Metal. Soc. AIME, 233, 1519.
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Input parameters
The inputs to this model is the composition in weight percentages.
- Carbon - C_Wt%
- wt. %.
- Aluminium - Al_Wt%
- wt. %.
- Silicon - Si_Wt%
- wt. %.
- Phosphorus - P_Wt%
- wt. %.
- Sulphur - S_Wt%
- wt. %.
- Vanadium - V_Wt%
- wt. %.
- Chromium - Cr_Wt%
- wt. %.
- Mangenese - Mn_Wt%
- wt. %.
- Cobolt - Co_Wt%
- wt. %.
- Nickel - Ni_Wt%
- wt. %.
- Copper - Cu_Wt%
- wt. %.
- Molybdenum - Mo_Wt%
- wt. %.
- Ruthenium - Ru_Wt%
- wt. %.
- Tungsten - W_Wt%
- wt. %.
- Temperature - Temp_K
- Kelvin.
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 - Predicted
- Angstroms
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None.
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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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Neuromat Ltd. offer a range of useful neural network software for making predictions and training models.
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1. Download the model
Download and uncompress the appropriate archive file file in a dedicated directory (for example: "neural").
On UNIX systems, this is done by:
- gzip -d MAP_NN_LP_AUS.tar.gz
- tar -xvf MAP_NN_LP_AUS.tar
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 Microsoft systems the model is run by the command:
model
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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neural networks, austenite, lattice parameter.
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Download source code for Unix
Download Microsoft Windows Executable
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