Computing energy #674
Replies: 11 comments 1 reply
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It's not very clear from your description what you did exactly. I guess you mean you used the |
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If so, you can post figures showing the training and testing results. Also the |
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So, I have some structures and I need to know energies of them. I read that I can use nep-code for this purpose if I have nep.txt file in the directory and prediction 1 key-word. I wanted to check this on structures that were used ifor test.xyz-file, when I trained nep-potential. And I have different errors... |
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Could you please post the two Also, could you tell me the versions of GPUMD if you did them using different versions. |
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The first nep.in file: The second nep.in file: |
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I have Version 3.8 |
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Then you can send me the inputs to debug (via email). |
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You must have used different versions of GPUMD for training and prediction and the two versions happen to have different default values for If you add |
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I got |
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Hello, dear Zheyong Fan! It has worked, thank you so much!!! I wanted ask you about one moment of MC. Could you tell me, please, what difference are between For example, I use this configuration: GPUMD does 1000 steps of MC in each step (from 1000000 in run-option)? When I used mc canonical 1 1 500 500 I got 1 or 0 in mcmd.out. But when I use more then 1 mc trials, I have float values in mcmd.out. |
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Hello, dear Zheyong Fan!
Could you tell me please, how can I compute energies of structures separately? I created a model.xyz-file with structure configurations (each structure about 64 atoms). After that I used nep-potential (nep.txt) file for computing, indicating prediction mode as 1. But I have some results:
(First column - prediction, second column - real values)
-12.2512 -21.6235
-14.1193 -21.2266
-14.2669 -20.5895
-14.0811 -20.6964
-12.0444 -20.6308
-15.2373 -19.5306
-13.6627 -19.9599
-11.9918 -20.9433
-13.6622 -19.9643
-13.6825 -19.9623
Structures were taken from test.xyz file used for training process. I can't understand, why errors in this case, when I predict energies separately from training, are significant. Thanks!
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