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OSError: Unable to create link (name already exists) #18

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eftalgezer opened this issue Sep 4, 2022 · 0 comments
Open

OSError: Unable to create link (name already exists) #18

eftalgezer opened this issue Sep 4, 2022 · 0 comments

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@eftalgezer
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eftalgezer commented Sep 4, 2022 •
edited
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====== Iteration 0 ======

Running SCF calculations ...
-----------------------------

converged SCF energy = -76.3545540706806
converged SCF energy = -76.3508207847105
converged SCF energy = -76.355707764332
converged SCF energy = -76.356824320776
converged SCF energy = -76.3739444533522
converged SCF energy = -76.3695047518221
converged SCF energy = -76.3694359857328
converged SCF energy = -76.3496333319949
converged SCF energy = -76.3557216068751
converged SCF energy = -76.3662805538731

Projecting onto basis ...
-----------------------------

workdir/0/pyscf.chkpt
workdir/1/pyscf.chkpt
workdir/2/pyscf.chkpt
workdir/3/pyscf.chkpt
workdir/4/pyscf.chkpt
workdir/5/pyscf.chkpt
workdir/6/pyscf.chkpt
workdir/7/pyscf.chkpt
workdir/8/pyscf.chkpt
workdir/9/pyscf.chkpt
10 systems found, adding 97a66c91908d8f76f249705362d9e536
10 systems found, adding energy
10 systems found, adding energy

Baseline accuracy
-----------------------------

{'mae': 0.05993, 'max': 0.09156, 'mean deviation': 0.0, 'rmse': 0.06635}

Fitting initial ML model ...
-----------------------------

Using symmetrizer  trace
Fitting 4 folds for each of 3 candidates, totalling 12 fits
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.737958  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.013578  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.012651  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.011192  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.009135  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.006535  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.003770  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.001574  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.000567  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.000380  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.000298  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.000238  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.000191  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.000144  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.000111  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.000086  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.000066  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.000051  Validation loss : 0.000000  Learning rate: 0.001
Epoch 18000 ||  Training loss : 0.000039  Validation loss : 0.000000  Learning rate: 0.001
Epoch 19000 ||  Training loss : 0.000101  Validation loss : 0.000000  Learning rate: 0.001
Epoch 20000 ||  Training loss : 0.000088  Validation loss : 0.000000  Learning rate: 0.001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.688702  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.005803  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.004593  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.004208  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.004022  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.003779  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.003422  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.002935  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.002444  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.002092  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.001836  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.001651  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.001514  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.001407  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.001320  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.001247  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.001183  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.001126  Validation loss : 0.000000  Learning rate: 0.001
Epoch 18000 ||  Training loss : 0.001074  Validation loss : 0.000000  Learning rate: 0.001
Epoch 19000 ||  Training loss : 0.001024  Validation loss : 0.000000  Learning rate: 0.001
Epoch 20000 ||  Training loss : 0.000981  Validation loss : 0.000000  Learning rate: 0.001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.172542  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.003400  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.002257  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.001851  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.001511  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.001225  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.000936  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.000696  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.000498  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.000386  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.000261  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.000219  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.000208  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.000247  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.000201  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.000199  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.001895  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.000876  Validation loss : 0.000000  Learning rate: 0.001
Epoch 18000 ||  Training loss : 0.000205  Validation loss : 0.000000  Learning rate: 0.001
Epoch 19000 ||  Training loss : 0.000184  Validation loss : 0.000000  Learning rate: 0.001
Epoch 20000 ||  Training loss : 0.000181  Validation loss : 0.000000  Learning rate: 0.001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.276985  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.001160  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.000992  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.000924  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.000869  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.000834  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.000810  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.000787  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.000763  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.000737  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.000716  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.000682  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.000656  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.000630  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.000606  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.000584  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.000562  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.000541  Validation loss : 0.000000  Learning rate: 0.001
Epoch 18000 ||  Training loss : 0.000522  Validation loss : 0.000000  Learning rate: 0.001
Epoch 19000 ||  Training loss : 0.000504  Validation loss : 0.000000  Learning rate: 0.001
Epoch 20000 ||  Training loss : 0.000487  Validation loss : 0.000000  Learning rate: 0.001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.624111  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.006432  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.005881  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.005858  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.005857  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.005869  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.005868  Validation loss : 0.000000  Learning rate: 0.001
Epoch 18000 ||  Training loss : 0.005859  Validation loss : 0.000000  Learning rate: 0.001
Epoch 19000 ||  Training loss : 0.005864  Validation loss : 0.000000  Learning rate: 0.001
Epoch 20000 ||  Training loss : 0.005861  Validation loss : 0.000000  Learning rate: 0.001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 1.096901  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.013148  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.005045  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.007128  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.007204  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 00014: reducing learning rate of group 0 to 1.0000e-04.
Epoch 13000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 15000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 16000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 17000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 18000 ||  Training loss : 0.007109  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 19000 ||  Training loss : 0.007109  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 20000 ||  Training loss : 0.007108  Validation loss : 0.000000  Learning rate: 0.0001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.441285  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.006409  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.006473  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.001
Epoch 00013: reducing learning rate of group 0 to 1.0000e-04.
Epoch 12000 ||  Training loss : 0.006475  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 14000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 15000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 16000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 17000 ||  Training loss : 0.006471  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 18000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 19000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 20000 ||  Training loss : 0.006472  Validation loss : 0.000000  Learning rate: 0.0001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.706089  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.009280  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.006735  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.006113  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.005982  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.005990  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 00017: reducing learning rate of group 0 to 1.0000e-04.
Epoch 16000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 18000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 19000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 20000 ||  Training loss : 0.005973  Validation loss : 0.000000  Learning rate: 0.0001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.692270  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.003213  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.001989  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.001688  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.001691  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.001728  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.001731  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.001728  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.001
Epoch 00015: reducing learning rate of group 0 to 1.0000e-04.
Epoch 14000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 16000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 17000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 18000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 19000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 20000 ||  Training loss : 0.001727  Validation loss : 0.000000  Learning rate: 0.0001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.419515  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.006821  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.003581  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.002444  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.002266  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.002343  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.002457  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.002566  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.002680  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.002774  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.002822  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.002834  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.002836  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.002836  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.002837  Validation loss : 0.000000  Learning rate: 0.001
Epoch 00016: reducing learning rate of group 0 to 1.0000e-04.
Epoch 15000 ||  Training loss : 0.002837  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.002837  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 17000 ||  Training loss : 0.002837  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 18000 ||  Training loss : 0.002837  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 19000 ||  Training loss : 0.002837  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 20000 ||  Training loss : 0.002837  Validation loss : 0.000000  Learning rate: 0.0001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 1.116178  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.017524  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.010454  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.009555  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.008318  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.006758  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.005142  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.003908  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.003240  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.002890  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.002633  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.002399  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.002211  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.002099  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.002061  Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.002053  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.002051  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.002051  Validation loss : 0.000000  Learning rate: 0.001
Epoch 18000 ||  Training loss : 0.002051  Validation loss : 0.000000  Learning rate: 0.001
Epoch 19000 ||  Training loss : 0.002051  Validation loss : 0.000000  Learning rate: 0.001
Epoch 20000 ||  Training loss : 0.002051  Validation loss : 0.000000  Learning rate: 0.001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.585081  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.008857  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.005610  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.004271  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.003184  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.002515  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.002223  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.002111  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.002070  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.002063  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.002062 Overwritten attributes  get_veff  of <class 'pyscf.dft.rks.RKS'>
 Validation loss : 0.000000  Learning rate: 0.001
Epoch 15000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Epoch 17000 ||  Training loss : 0.002109  Validation loss : 0.000000  Learning rate: 0.001
Epoch 18000 ||  Training loss : 0.003392  Validation loss : 0.000000  Learning rate: 0.001
Epoch 19000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Epoch 20000 ||  Training loss : 0.002062  Validation loss : 0.000000  Learning rate: 0.001
Activation unknown, defaulting to GELU
ModuleDict(
  (X): Linear(in_features=19, out_features=1, bias=True)
)
Epoch 0 ||  Training loss : 0.401415  Validation loss : 0.000000  Learning rate: 0.001
Epoch 1000 ||  Training loss : 0.004100  Validation loss : 0.000000  Learning rate: 0.001
Epoch 2000 ||  Training loss : 0.003189  Validation loss : 0.000000  Learning rate: 0.001
Epoch 3000 ||  Training loss : 0.003003  Validation loss : 0.000000  Learning rate: 0.001
Epoch 4000 ||  Training loss : 0.002946  Validation loss : 0.000000  Learning rate: 0.001
Epoch 5000 ||  Training loss : 0.002949  Validation loss : 0.000000  Learning rate: 0.001
Epoch 6000 ||  Training loss : 0.002954  Validation loss : 0.000000  Learning rate: 0.001
Epoch 7000 ||  Training loss : 0.002955  Validation loss : 0.000000  Learning rate: 0.001
Epoch 8000 ||  Training loss : 0.002957  Validation loss : 0.000000  Learning rate: 0.001
Epoch 9000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.001
Epoch 10000 ||  Training loss : 0.002957  Validation loss : 0.000000  Learning rate: 0.001
Epoch 11000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.001
Epoch 12000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.001
Epoch 13000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.001
Epoch 14000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.001
Epoch 00016: reducing learning rate of group 0 to 1.0000e-04.
Epoch 15000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.001
Epoch 16000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 17000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 18000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 19000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.0001
Epoch 20000 ||  Training loss : 0.002956  Validation loss : 0.000000  Learning rate: 0.0001


====== Iteration 1 ======
Using symmetrizer  trace
Success!

Running SCF calculations ...
-----------------------------

NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3529189373852
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3482785744807
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3557948619506
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3566029382129
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3773105081291
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3717756096372
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3728823731244
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3468435329547
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.355468924666
NeuralXC: Loading model from /home/egezer/neuralxc/examples/quickstart/sc/model_it1.jit
NeuralXC: Model successfully loaded
converged SCF energy = -76.3696142171586

Projecting onto basis...
-----------------------------

workdir/0/pyscf.chkpt
workdir/1/pyscf.chkpt
workdir/2/pyscf.chkpt
workdir/3/pyscf.chkpt
workdir/4/pyscf.chkpt
workdir/5/pyscf.chkpt
workdir/6/pyscf.chkpt
workdir/7/pyscf.chkpt
workdir/8/pyscf.chkpt
workdir/9/pyscf.chkpt
10 systems found, adding 97a66c91908d8f76f249705362d9e536
Traceback (most recent call last):
  File "/home/egezer/.local/bin/neuralxc", line 7, in <module>
    exec(compile(f.read(), __file__, 'exec'))
  File "/home/egezer/neuralxc/bin/neuralxc", line 240, in <module>
    func(**args_dict)
  File "/home/egezer/neuralxc/neuralxc/drivers/model.py", line 266, in sc_driver
    pre_driver(
  File "/home/egezer/neuralxc/neuralxc/drivers/other.py", line 210, in pre_driver
    add_data_driver(hdf5=file, system=system, method=method, density=filename, add=[], traj=xyz, override=True)
  File "/home/egezer/neuralxc/neuralxc/drivers/data.py", line 81, in add_data_driver
    obs(observable, zero)
  File "/home/egezer/neuralxc/neuralxc/drivers/data.py", line 74, in obs
    add_density((density.split('/')[-1]).split('.')[0], file, data, system, method, override)
  File "/home/egezer/neuralxc/neuralxc/datastructures/hdf5.py", line 19, in add_density
    return add_data(key, *args, **kwargs)
  File "/home/egezer/neuralxc/neuralxc/datastructures/hdf5.py", line 97, in add_data
    create_dataset()
  File "/home/egezer/neuralxc/neuralxc/datastructures/hdf5.py", line 94, in create_dataset
    cg.create_dataset(which, data=data)
  File "/home/egezer/.local/lib/python3.10/site-packages/h5py/_hl/group.py", line 139, in create_dataset
    self[name] = dset
  File "/home/egezer/.local/lib/python3.10/site-packages/h5py/_hl/group.py", line 371, in __setitem__
    h5o.link(obj.id, self.id, name, lcpl=lcpl, lapl=self._lapl)
  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "h5py/h5o.pyx", line 202, in h5py.h5o.link
OSError: Unable to create link (name already exists)
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