
=====================
Starting a PyAMFF job
=====================

    0.01s: Reading inputs
             Fingerprints used:
             Type:  BP
             Li:   2 G1s   2 G2s
    0.03s: Processing training data
             Number of training images: 69
    0.20s: Calculating fingerprints
    9.06s: Defining machine-learning model
             Model type: neural_network
             Model structure: 2
             Creating model from saved potential
             Energy coefficient: 1.000000
             Force coefficent:   0.050000
             Energy tolerance:   0.000100
             Force tolerance:    0.010000
             Loss tolerance:     0.023853
             Parallelization setup:
               Number of processes:           2
               Total number of batches:       2
               Number of batches per process: 1
    9.07s: Partitioning data
    9.49s: Training started
             Epoch    LossValue   EnergyRMSE    ForceRMSE
           0     0.561799     0.560296     0.183639
           1     0.094069     0.059778     0.324825
           2     0.086703     0.052490     0.308619
           3     0.084014     0.051708     0.296127
           4     0.071631     0.049809     0.230222
           5     0.054913     0.045320     0.138678
           6     0.037335     0.023281     0.130529
           7     0.032658     0.012922     0.134132
           8     0.032520     0.013412     0.132491
           9     0.032251     0.013719     0.130531
          10     0.030767     0.014721     0.120825
          11     0.029733     0.015968     0.112168
          12     0.028193     0.015301     0.105901
          13     0.027048     0.011623     0.109223
          14     0.026927     0.012196     0.107362
          15     0.026837     0.012319     0.106628
          16     0.025930     0.012792     0.100868
          17     0.025394     0.012535     0.098763
          18     0.024968     0.010778     0.100719
          19     0.024731     0.010070     0.101014
          20     0.024503     0.010149     0.099742
          21     0.024451     0.010074     0.099633
          22     0.024550     0.010258     0.099748
          23     0.024491     0.010106     0.099769
          24     0.024464     0.010067     0.099715
          25     0.024472     0.010046     0.099798
          26     0.024511     0.010082     0.099915
          27     0.024457     0.009994     0.099824
          28     0.024451     0.010024     0.099737
          29     0.024464     0.010029     0.099788
          30     0.024459     0.010008     0.099810
          31     0.024458     0.010015     0.099789
          32     0.024494     0.010204     0.099580
          33     0.051270     0.040403     0.141154
          34     0.035341     0.027924     0.096870
          35     0.036169     0.022573     0.126388
          36     0.030053     0.018320     0.106545
          37     0.075933     0.072445     0.101747
          38     0.057309     0.044840     0.159606
          39     0.043694     0.033929     0.123127
          40     0.708508     0.686665     0.780711
          41     0.029545     0.018552     0.102836
          42     0.025114     0.010502     0.102023
          43     0.024779     0.009317     0.102683
          44     0.024674     0.009284     0.102234
          45     0.023870     0.009346     0.098230
          46     0.025319     0.012762     0.097795
          47     0.023891     0.009647     0.097745
          48     0.023876     0.009558     0.097850
          49     0.023861     0.009521     0.097847
             Max Epoch Reached
   13.41s: Training done, time used: 3.92s
