Property prediction model training fails when training data is much similar #5021
Description
Summary
A researcher trains a d33(piezoelectric coefficient) prediction model with 2 training data using property prediction head. The two data is perovskite-type(ABO3). The composition(Pb1000Ti340Nb400Mg140In120O3000) and the spatial arrangement of atoms of the two data are completely the same. The only difference between the two data is that Ti,Nb,Mg,In randomly occupy the B sites in 10✖️10✖️10 perovskite supercells. The two pictures shows the x-direction projection of B sites in the two data. I use StructureMatcher of pymatgen to confirm that the two structure can not match. The label of the two data is 411 and 587, respectively.
I use the settings of DPA-3.1-3M and train 1000 steps(500 epoch), but find there is no precision, the dp test result is
# d33_database/1 - 0: data_property pred_property
4.111499938964843750e+02 4.762894698179545685e+02
# d33_database/2 - 0: data_property pred_property
5.865300292968750000e+02 4.762894713410120744e+02
It seems that dp model can not distinguish the two similar structure.
The reproduce input.json and data file are:
input.json
d33_database.tgz
DeePMD-kit Version
3.1.0
Backend and its version
pytorch
Python Version, CUDA Version, GCC Version, LAMMPS Version, etc
Python version: 3.12.11
CUDA Version: 12.4
Details
See above