error in MLFF

[yingma]

Dear admins,

I'm trying to develop a MLFF for amorphous Si. I performed on-the-fly training, followed by a refit calculation to generate MLFF. The number of configurations is 1629, and number of basis sets is 6784. From ML_LOGFILE for the refit calculation, the error in energy is 10.3 meV/atom and error in force is 0.15 eV/Angstrom.

Using the generated MLFF, I run an MD of amorphous Si. From the trajectory of the simulation, I selected 50 configurations. For each configuration, I performed single point DFT and MLFF calculations, based on which the mean absolute error and max error were calculated. The mean absolute error in energy is 10.6 meV/atom and in force is 0.14 eV/Angstrom, which seems to be consistent with those from the refit calculation. However, the max error in energy is 28.6 meV/atom, and 0.92 eV/Angstrom in force.

So my questions are:
1) The max error in energy and in force are much larger than the mean. For sure max error will be larger, but in my case the max error in force is almost 7 times larger than the mean - is this behavior normal? Can I trust the result from the MLFF? If not, any suggestions on how to minimize the max error?
2) VASP manual recommend an energy error of less than 1 meV/atom and force error of less than 0.1 eV/Angstrom. The errors I have are beyond those recommended values. Since I'm modeling amorphous materials, I guess a larger error is expected. What are typically errors for amorphous materials? Do you have any recommendation on how to minimize the training error?
3) I have 1629 configurations and 6784 local references. Are those numbers reasonable for amorphous materials?

Any insights will be greatly appreciated. Thank you very much.

[christopher_sheldon1]

Dear Yingma,

Thank you for your question. That is quite a large value. The max error being 7x larger isn't necessarily an issue. The mean error is more important. Could you upload your OUTCAR, POSCAR, KPOINTS, INCAR, and stdout files so that I can look into this further?

I am not sure about the number of configurations. It is a rather large number but I would have to look at your input and output files to answer more precisely.

Best wishes,

Chris

[yingma]

Hi Chris,

Thank you very much for your response. Please find all files using this https://universityofwieauclaire-my.shar ... M?e=IZ5FMM

Here are more details on my calculations: I used the melt-quench method to generate amorphous Si. All melt-quench simulations were performed using AIMD. Six 300K amorphous Si structures are selected for MLFF training (POSCAR.1 to POSCAR.6). For each POSCAR, three scaling factors, 0.95, 1.0, and 1.05 were used, and thus there are totaling 15 structures used for on-the-fly training. Each training was performed at 500K for 2 ps. ML_ABN and ML_LOGFILE from each training are saved in folder savedfile.

After training, the ML_ABN from the last training was used for refit calculation. The ML_FF file from refit is then used to run MD simulations for each of the POSCAR. The errors I mentioned in my previous post is from POSCAR.3, but other POSCARs show similar behavior. The summary of errors is in file "applyerror.dat".

Maybe the large error is related to the way I train the MLFF. Any recommendations on how to properly train MLFF for amorphous materials would also be greatly appreciated.

Thank you very much!