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Commit 56d057e8 authored by Jan Janssen's avatar Jan Janssen
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Clean up day3

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day_3/RuNNerMD.ipynb deleted 100644 → 0
View file @ 8c04e90a
%% Cell type:code id:suffering-touch tags:
``` python
from pyiron import Project
```
%% Cell type:code id:geological-bankruptcy tags:
``` python
pr = Project('md_run')
```
%% Cell type:code id:prompt-evaluation tags:
``` python
j = pr.create.job.Lammps("md", delete_existing_job=True)
```
%% Cell type:code id:governing-consortium tags:
``` python
j.potential = Project('../day_2/02-runner/runner_fit').load('fit').lammps_potential
```
%% Cell type:code id:authentic-moscow tags:
``` python
j.structure = pr.create.structure.ase_bulk('Cu', cubic=True).repeat(3)
```
%% Cell type:code id:tamil-witch tags:
``` python
j.structure.plot3d()
```
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id:fatty-asbestos tags:
``` python
j.calc_md(n_ionic_steps=1000, temperature=500)
```
%% Cell type:code id:under-shirt tags:
``` python
%%time
j.run()
```
%% Cell type:code id:liked-thomson tags:
``` python
j.animate_structure()
```
%%%% Output: display_data
day_3/export.csv deleted 100644 → 0
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,id,status,chemicalformula,job,subjob,project,timestart,timestop,totalcputime,computer,hamilton,hamversion,parentid,masterid
0,0,finished,,df1_A1_A2_A3_EV_elast_phon,/df1_A1_A2_A3_EV_elast_phon,Cu_training_archive/Cu_database,2021-02-08 10:33:52.341472,,,zora@cmti001#1,GenericJob,0.4,,
1,1,finished,,df3_10k,/df3_10k,Cu_training_archive/Cu_database,2021-02-08 10:33:53.993230,,,zora@cmti001#1,GenericJob,0.4,,
2,2,finished,,df2_1k,/df2_1k,Cu_training_archive/Cu_database,2021-02-08 10:33:54.435308,,,zora@cmti001#1,GenericJob,0.4,,
day_3/fit/input.nn deleted 100644 → 0
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### ####################################################################################################################
### This is the input file for the RuNNer tutorial (POTENTIALS WORKSHOP 2021-03-10)
### This input file is intended for release version 1.2
### RuNNer is hosted at www.gitlab.com. The most recent version can only be found in this repository.
### For access please contact Prof. Jörg Behler, joerg.behler@uni-goettingen.de
###
### ####################################################################################################################
### General remarks:
### - commands can be switched off by using the # character at the BEGINNING of the line
### - the input file can be structured by blank lines and comment lines
### - the order of the keywords is arbitrary
### - if keywords are missing, default values will be used and written to runner.out
### - if mandatory keywords or keyword options are missing, RuNNer will stop with an error message
###
########################################################################################################################
########################################################################################################################
### The following keywords just represent a subset of the keywords offered by RuNNer
########################################################################################################################
########################################################################################################################
########################################################################################################################
### general keywords
########################################################################################################################
nn_type_short 1 # 1=Behler-Parrinello
runner_mode 3 # 1=calculate symmetry functions, 2=fitting mode, 3=predicition mode
number_of_elements 1 # number of elements
elements Cu # specification of elements
random_seed 10 # integer seed for random number generator
random_number_type 6 # 6 recommended
########################################################################################################################
### NN structure of the short-range NN
########################################################################################################################
use_short_nn # use NN for short range interactions
global_hidden_layers_short 2 # number of hidden layers
global_nodes_short 15 15 # number of nodes in hidden layers
global_activation_short t t l # activation functions (t = hyperbolic tangent, l = linear)
########################################################################################################################
### symmetry function generation ( mode 1):
########################################################################################################################
test_fraction 0.10000 # threshold for splitting between fitting and test set
########################################################################################################################
### symmetry function definitions (all modes):
########################################################################################################################
cutoff_type 1
symfunction_short Cu 2 Cu 0.000000 0.000000 12.000000
symfunction_short Cu 2 Cu 0.006000 0.000000 12.000000
symfunction_short Cu 2 Cu 0.016000 0.000000 12.000000
symfunction_short Cu 2 Cu 0.040000 0.000000 12.000000
symfunction_short Cu 2 Cu 0.109000 0.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 1.000000 1.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 1.000000 2.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 1.000000 4.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 1.000000 16.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 1.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 2.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 4.000000 12.000000
symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 16.000000 12.000000
########################################################################################################################
### fitting (mode 2):general inputs for short range AND electrostatic part:
########################################################################################################################
epochs 20 # number of epochs
fitting_unit eV # unit for error output in mode 2 (eV or Ha)
precondition_weights # optional precondition initial weights
########################################################################################################################
### fitting options ( mode 2): short range part only:
########################################################################################################################
short_energy_error_threshold 0.10000 # threshold of adaptive Kalman filter short E
short_force_error_threshold 1.00000 # threshold of adaptive Kalman filter short F
kalman_lambda_short 0.98000 # Kalman parameter short E/F, do not change
kalman_nue_short 0.99870 # Kalman parameter short E/F, do not change
use_short_forces # use forces for fitting
repeated_energy_update # optional: repeat energy update for each force update
mix_all_points # do not change
scale_symmetry_functions # optional
center_symmetry_functions # optional
short_force_fraction 0.01 #
force_update_scaling -1.0 #
########################################################################################################################
### output options for mode 2 (fitting):
########################################################################################################################
write_trainpoints # write trainpoints.out and testpoints.out files
write_trainforces # write trainforces.out and testforces.out files
########################################################################################################################
### output options for mode 3 (prediction):
########################################################################################################################
calculate_forces # calculate forces
calculate_stress # calculate stress tensor
day_3/fit/scaling.data deleted 100644 → 0
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1 1 14.256833324 19.604083786 16.584187995
1 2 10.772870436 14.917517522 12.575789477
1 3 7.067719199 9.940850811 8.325756144
1 4 3.063546892 4.514143322 3.699677080
1 5 0.443438674 0.840539370 0.611310762
1 6 11.372207256 23.338043099 16.099109800
1 7 30.908289966 62.610854904 43.470717798
1 8 4.747847116 10.201151151 6.890568480
1 9 24.299123692 49.473962957 34.262176478
1 10 1.690730707 3.912715044 2.559298728
1 11 16.549052901 34.322241783 23.555567314
1 12 0.252821214 0.820637834 0.460356083
1 13 4.065311900 9.687837138 6.245172716
-0.1359667057 -0.1284178204
day_3/fit/weights.029.data deleted 100644 → 0
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