Open singraber opened 3 years ago
Merging #92 (15a7548) into master (995f0b5) will decrease coverage by
0.17%
. The diff coverage isn/a
.
@@ Coverage Diff @@
## master #92 +/- ##
==========================================
- Coverage 71.76% 71.59% -0.18%
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Files 129 129
Lines 13981 14065 +84
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+ Hits 10034 10070 +36
- Misses 3947 3995 +48
Flag | Coverage Δ | |
---|---|---|
cpp | 74.28% <ø> (-0.27%) |
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python | 71.59% <ø> (-0.18%) |
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Flags with carried forward coverage won't be shown. Click here to find out more.
Impacted Files | Coverage Δ | |
---|---|---|
src/libnnp/Atom.cpp | 40.54% <ø> (-0.40%) |
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src/libnnp/Atom.h | 66.66% <ø> (ø) |
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src/libnnp/Element.h | 100.00% <ø> (ø) |
|
src/libnnp/Mode.cpp | 74.59% <ø> (-1.28%) |
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src/libnnp/Mode.h | 66.66% <ø> (ø) |
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src/libnnp/Settings.cpp | 94.22% <ø> (+0.05%) |
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src/libnnp/Structure.cpp | 76.99% <ø> (-1.54%) |
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src/libnnp/Vec3D.h | 88.40% <0.00%> (-5.45%) |
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... and 6 more |
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I would be interested in trying this feature out, especially if it has a lammps interface. How close to working is it?
I did not yet have time to look over the recent changes, I will do this next week. From what I can see the LAMMPS interface is not yet included but the standalone prediction (using nnp-predict
) should work. @Kyvala Do you also want to comment on this?
I have tested it and it should work. However, it is implemented only for nnp-predict. It should be straightforward to implement even for LAMMPS interface but I am waiting on Andreas's check and finding optimal parameters for my material. Then I will move on to LAMMPS and make it work even for LAMMPS.
Merging #92 (0ebd71f) into master (9c350fb) will decrease coverage by
0.48%
. The diff coverage isn/a
.
@@ Coverage Diff @@
## master #92 +/- ##
==========================================
- Coverage 72.28% 71.80% -0.49%
==========================================
Files 130 130
Lines 14231 14419 +188
==========================================
+ Hits 10287 10353 +66
- Misses 3944 4066 +122
Flag | Coverage Δ | |
---|---|---|
cpp | 74.42% <ø> (-0.66%) |
:arrow_down: |
python | 71.80% <ø> (-0.49%) |
:arrow_down: |
Flags with carried forward coverage won't be shown. Click here to find out more.
Impacted Files | Coverage Δ | |
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src/application/nnp-comp2.cpp | 0.00% <ø> (ø) |
|
src/application/nnp-predict.cpp | 82.92% <ø> (-12.59%) |
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src/application/nnp-train.cpp | 94.28% <ø> (+0.16%) |
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src/libnnp/Atom.cpp | 40.75% <ø> (-0.20%) |
:arrow_down: |
src/libnnp/Atom.h | 66.66% <ø> (ø) |
|
src/libnnp/Element.h | 100.00% <ø> (ø) |
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src/libnnp/Mode.cpp | 74.04% <ø> (-1.78%) |
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src/libnnp/Mode.h | 66.66% <ø> (ø) |
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src/libnnp/Prediction.cpp | 69.46% <ø> (-30.54%) |
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src/libnnp/Settings.cpp | 94.22% <ø> (+0.05%) |
:arrow_up: |
... and 14 more |
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Hi developers,
I've just stumbled upon this PR again and was wondering if there are any plans to merge this with the main branch in the near future. From a quick look at the code, it seems like it's very close to completion.
Many thanks for all your hard work and all the best, Christoph
Dear Christoph Schran,
The committee implementation is working and complete for prediction. We are using it. The official merging is not happening soon as there are conflicts and unexpected behavior in the training phase. Those have to be resolved first. However, if you are interested in prediction, you can merge it with the master version by yourself and use it only for prediction. There should not be a critical conflict.
Best regards, Lukas Kyvala
po 28. 11. 2022 v 12:48 odesílatel Christoph Schran < @.***> napsal:
Hi developers,
I've just stumbled upon this PR again and was wondering if there are any plans to merge this with the main branch in the near future. From a quick look at the code, it seems like it's very close to completion.
Many thanks for all your hard work and all the best, Christoph
— Reply to this email directly, view it on GitHub https://github.com/CompPhysVienna/n2p2/pull/92#issuecomment-1328943205, or unsubscribe https://github.com/notifications/unsubscribe-auth/AQ3JUWYNVHDGGC5AOLGZ6PTWKSL2RANCNFSM4YYLTJ7A . You are receiving this because you were mentioned.Message ID: @.***>
Implement committee NNPs (C-NNPs) as presented in J. Chem. Phys. 153, 104105 (2020) in the core library, applications (only prediction, no training) and LAMMPS interface.
Allow two different modes in LAMMPS (and tools where useful):
New keywords introduced:
committee_mode
, arguments can bevalidation
orprediction
committee_data
, two arguments: