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

    0.02s: Reading inputs
             Fingerprints used:
             Type:  BP
             Li:   2 G1s   2 G2s
    0.04s: Processing training data
             Number of training images: 69
    0.24s: Calculating fingerprints
    9.38s: 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
    9.39s: Partitioning data
    9.62s: Training started
             Epoch    LossValue   EnergyRMSE    ForceRMSE
                 1     0.561799     0.560296     0.183639
                 2     0.544213     0.542570     0.188949
                 3     0.483837     0.481514     0.211763
                 4     0.431874     0.428638     0.236002
                 5     0.369285     0.364329     0.269666
                 6     0.295353     0.286857     0.314527
                 7     0.239355     0.224454     0.371787
                 8     0.186138     0.158585     0.435851
                 9     0.154350     0.102810     0.514861
                10     0.149431     0.098021     0.504413
                11     0.130906     0.082360     0.455045
                12     0.139493     0.105806     0.406526
                13     0.136681     0.105403     0.389150
                14     0.121699     0.084399     0.392106
                15     0.110505     0.068804     0.386712
                16     0.103616     0.059798     0.378428
                17     0.096679     0.055461     0.354145
                18     0.091592     0.053449     0.332632
                19     0.091374     0.072871     0.246535
                20     0.068248     0.040016     0.247244
                21     0.063511     0.037100     0.230530
                22     0.055125     0.040551     0.166994
                23     0.044604     0.026869     0.159222
                24     0.044870     0.030930     0.145371
                25     0.037828     0.024256     0.129816
                26     0.049339     0.041892     0.116573
                27     0.049339     0.041892     0.116573
                28     0.035574     0.023095     0.121005
                29     0.035659     0.020334     0.131001
                30     0.035872     0.020940     0.130253
                31     0.032589     0.016971     0.124421
                32     0.032584     0.018707     0.119311
                33     0.032037     0.017808     0.119099
                34     0.031487     0.016108     0.120992
                35     0.031545     0.016363     0.120613
                36     0.030787     0.015541     0.118854
                37     0.029956     0.014466     0.117313
                38     0.029615     0.014321     0.115928
                39     0.029307     0.013850     0.115505
                40     0.028911     0.013262     0.114889
                41     0.028587     0.012736     0.114457
                42     0.028365     0.012263     0.114386
                43     0.028237     0.011711     0.114908
                44     0.028229     0.011321     0.115650
                45     0.028186     0.011358     0.115367
                46     0.028180     0.011465     0.115125
                47     0.028091     0.011436     0.114743
                48     0.028080     0.011638     0.114282
                49     0.028068     0.011840     0.113812
                50     0.028000     0.011685     0.113795
             Max Epoch Reached
   13.52s: Training done, time used: 3.90s
