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element profile/hyperparameter optimization #385
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Hi Rana, I can give two pieces of advice:
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Thanks for your suggestion. It is fast now! |
Happy to know that it helps! I used "24" as an example, as there are 24 cores on each node of the computer cluster resources our group have access to. This value should be modified on different machines to achieve best efficiency. |
Dear Ji Qi, Ok, now I am seeing that multiprocessing is at least three times slower than 'n_jobs'=24 of sci-kit learn for the larger dataset. Best regards, |
The describer here is the local environment describer of the SNAP potential, which describes the material structures in a math form. The LinearRegression is the model used in the ML training process to connect the local environment describers (input) to target properties, which are energies, forces and stresses. For parameter tuning, I'm not aware any existing automatic algorithms for SNAP training. Please let me know if there is, which I would be interested in. In previous works from our group, we used |
Dear Developers,
I am trying to optimize the element profile for a multicomponent system.
I am a very beginner in python doing this (manually) by python 'for loop'.
I am afraid that it will take 15 years to be finished (200x200x200 number of searches).
I am seeing that authors previously did it for several multicomponent systems.
Could you suggest to us some efficient and faster way to do it?
####################
loop
rcut_grid = []
for rc_1 in np.arange(4,6,0.01):
for rc_2 in np.arange(4,6,0.01):
for rc_3 in np.arange(4,6,0.01):
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