ML LOGFILE: Difference between revisions
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Every VASP run with activated machine learning ([[INCAR]] contains <code>{{TAG|ML_LMLFF}} = .TRUE.</code>) will generate a file called {{TAG|ML_LOGFILE}}. In this log file a summary of settings and the development of quantities related to machine learning are presented in a compact, yet human-readable and post-processing friendly way. It complements the usual ab initio log output in the [[OUTCAR]] and [[OSZICAR]] files for machine learning VASP runs. | |||
<!-- This file contains the main output for the machine learning force field method. | <!-- This file contains the main output for the machine learning force field method. | ||
Most importantly it contains the error on energies, forces and stress tensors. The error is calculated as the mean error on the training data between ab initio and force field calculations. --> | Most importantly it contains the error on energies, forces and stress tensors. The error is calculated as the mean error on the training data between ab initio and force field calculations. --> | ||
== File layout == | == File layout == | ||
{{BOX|warning|Until machine learning is officially released in {{VASP}} the {{TAG|ML_LOGFILE}} file format may change without notice!}} | {{BOX|warning|Until machine learning is officially released in {{VASP}} the {{TAG|ML_LOGFILE}} file format may change without notice!}} | ||
The machine learning log file is split into multiple sections, visually separated like this: | |||
* SECTION TITLE **************************************************************************************************************************** | |||
... content ... | |||
******************************************************************************************************************************************** | |||
The actual composition of log sections may depend on the machine learning mode of operation (see {{TAG|ML_ISTART}}) | |||
=== Memory consumption estimation === | === Memory consumption estimation === | ||
Revision as of 08:48, 8 October 2021
Every VASP run with activated machine learning (INCAR contains ML_LMLFF = .TRUE.) will generate a file called ML_LOGFILE. In this log file a summary of settings and the development of quantities related to machine learning are presented in a compact, yet human-readable and post-processing friendly way. It complements the usual ab initio log output in the OUTCAR and OSZICAR files for machine learning VASP runs.
File layout
| Warning: Until machine learning is officially released in VASP the ML_LOGFILE file format may change without notice! |
The machine learning log file is split into multiple sections, visually separated like this:
* SECTION TITLE **************************************************************************************************************************** ... content ... ********************************************************************************************************************************************
The actual composition of log sections may depend on the machine learning mode of operation (see ML_ISTART)
Memory consumption estimation
* MEMORY INFORMATION *********************************************************************************************************************** Estimated memory consumption for ML force field generation (MB): Persistent allocations for force field : 516.9 | |-- CMAT for basis : 20.3 |-- FMAT for basis : 458.5 |-- DESC for basis : 2.6 |-- SOAP matrix : 2.3 Persistent allocations for ab initio data : 8.1 | |-- Ab initio data : 7.8 |-- Ab initio data (new) : 0.3 Temporary allocations for sparsification : 460.9 | |-- SVD matrices : 28.0 Other temporary allocations : 15.5 | |-- Descriptors : 4.7 |-- Regression : 6.5 |-- Prediction : 4.2 Total memory consumption : 1001.4 ********************************************************************************************************************************************
Machine learning setup
* MACHINE LEARNING SETTINGS **************************************************************************************************************** This section lists the available machine-learning related settings with a short description, their selected values and the INCAR tags. The column between the value and the INCAR tag may contain a "state indicator" highlighting the origin of the value. Here is a list of possible indicators: * : (empty) Tag was not provided in the INCAR file, a default value was chosen automatically. * (I) : Value was provided in the INCAR file. * (i) : Value was provided in the INCAR file, deprecated tag. * (!) : A value found in the INCAR file was overwritten by the contents of the ML_FF file. * (?) : The value for this tag was never set (please report this to the VASP developers). Tag values with associated units are given here in Angstrom/eV, if not specified otherwise. Please refer to the VASP online manual for a detailed description of available INCAR tags. General settings -------------------------------------------------------------------------------------------------------------------------------------------- Machine learning operation mode : 0 (I) ML_ISTART Descriptor settings -------------------------------------------------------------------------------------------------------------------------------------------- Radial descriptors: ------------------- Cutoff radius of radial descriptors : 5.00000E+00 ML_RCUT1 Gaussian width for broadening the atomic distribution for radial descriptors : 5.00000E-01 ML_SION1 Number of radial basis functions for atomic distribution for radial descriptors : 8 ML_MRB1 Angular descriptors: -------------------- Cutoff radius of angular descriptors : 5.00000E+00 ML_RCUT2 Gaussian width for broadening the atomic distribution for angular descriptors : 5.00000E-01 ML_SION2 Number of radial basis functions for atomic distribution for angular descriptors : 8 ML_MRB2 Maximum angular momentum quantum number of spherical harmonics used to expand atomic distributions : 4 ML_LMAX2 ...
Existing ab initio data
* AVAILABLE AB INITIO DATA ***************************************************************************************************************** Number of stored (maximum) ab initio structures: 114 ( 1500) * System 1 : 114 , name: "Si cubic diamond 2x2x2 super cell" * System 2 : 0 , name: "Si cubic diamond 2x2x2 super cell" Maximum number of atoms per element: * Element Si : 64 ********************************************************************************************************************************************
Main loop
Header
* MAIN LOOP ******************************************************************************************************************************** # STATUS ############################################################### # STATUS This line describes the overall status of each step. # STATUS # STATUS nstep ..... MD time step or input structure counter # STATUS state ..... One-word description of step action # STATUS - "accurate" (1) : Errors are low, force field is used # STATUS - "threshold" (2) : Errors exceeded threshold, structure is sampled from ab initio # STATUS - "learning" (3) : Stored configurations are used for training force field # STATUS - "critical" (4) : Errors are high, ab initio sampling and learning is enforced # STATUS - "predict" (5) : Force field is used in prediction mode only, no error checking # STATUS is ........ Integer representation of above one-word description (integer in parenthesis) # STATUS doabin .... Perform ab initio calculation (T/F) # STATUS iff ....... Force field available (T/F, False after startup hints to possible convergence problems) # STATUS nsample ... Number of steps since last reference structure collection (sample = T) # STATUS ngenff .... Number of steps since last force field generation (genff = T) # STATUS ############################################################### # STATUS nstep state is doabin iff nsample ngenff # STATUS 2 3 4 5 6 7 8 # STATUS ###############################################################
Body
-------------------------------------------------------------------------------- STATUS 82 learning 3 T T 0 72 LCONF 82 Si 1222 1228 SPRSC 82 129 129 Si 1228 1224 REGR 82 1 1 1.27238822E+00 5.73175466E-02 7.83203623E-12 REGR 82 1 2 1.28510216E+00 5.73084508E-02 7.75332075E-12 REGRF 82 1 3 1.29486873E+00 5.73015362E-02 7.69391276E-12 2.23430718E+16 5.75166077E+09 STDAB 82 1.28851006E-01 1.02791005E+00 1.07081172E+01 ERR 82 1.21269596E-02 2.35740491E-01 4.40365370E+00 CFE 82 2.71935242E-01 2.20681769E-01 7.30391193E-01 LASTE 82 1.63070075E-02 2.66475855E-01 7.17595981E+00 BEE 82 4.72039040E-05 1.03291046E-01 3.02999592E-02 9.56824349E-02 6.23077315E-01 4.66683801E-01 THRHIST 82 1 8.45535075E-02 THRHIST 82 2 8.99995395E-02 THRHIST 82 3 9.42765991E-02 THRHIST 82 4 9.37027237E-02 THRHIST 82 5 9.78682111E-02 THRHIST 82 6 1.02991465E-01 THRHIST 82 7 1.04972577E-01 THRHIST 82 8 1.02574658E-01 THRHIST 82 9 9.68150073E-02 THRHIST 82 10 8.90700596E-02 THRUPD 82 9.54674570E-02 9.56824349E-02 6.60216623E-02 1.06906899E-02 BEEF 82 4.58511233E-05 9.95065359E-02 2.94732909E-02 9.56824349E-02 6.03276708E-01 4.51396163E-01 --------------------------------------------------------------------------------
-------------------------------------------------------------------------------- STATUS 63 accurate 1 F T 3 53 BEEF 63 4.67236540E-05 1.09788403E-01 2.90204790E-02 9.56824349E-02 6.29349214E-01 4.74949548E-01 --------------------------------------------------------------------------------
Timing information
* TIMING INFORMATION *********************************************************************************************************************** Program part system clock (sec) cpu time (sec) ---------------------------------------------------|--------------------|------------------- Setup (file I/O, parameters,...) | 0.242 | 0.240 Descriptor and design matrix | 10.540 | 10.536 Sparsification of configurations | 9.183 | 9.177 Regression | 14.778 | 14.770 Prediction | 32.461 | 32.450 ---------------------------------------------------|--------------------|------------------- TOTAL | 67.204 | 67.173 ********************************************************************************************************************************************
Post-processing usage
# ERR ###################################################################### # ERR This line contains the RMSEs of the predictions with respect to ab initio results for the training data. # ERR # ERR nstep ......... MD time step or input structure counter # ERR rmse_energy ... RMSE of energies (eV atom^-1) # ERR rmse_force .... RMSE of forces (eV Angst^-1) # ERR rmse_stress ... RMSE of stress (kB) # ERR ###################################################################### # ERR nstep rmse_energy rmse_force rmse_stress # ERR 2 3 4 5 # ERR ###################################################################### ERR 2 8.77652825E-05 1.00592308E-02 2.68800480E-02 ERR 3 3.01865279E-05 1.06283576E-02 5.81209819E-02 ERR 4 1.52820686E-04 1.31384993E-02 1.10439716E-01 ERR 5 1.62739008E-04 1.74252575E-02 1.40488725E-01 ERR 6 2.97462508E-04 2.32615279E-02 1.79092561E-01 ERR 7 2.10891509E-04 2.79123925E-02 1.94566420E-01 ERR 8 3.26150852E-04 3.15081244E-02 1.76637577E-01 ERR 9 7.03479132E-04 3.42249550E-02 1.66830771E-01 ERR 10 2.41808229E-04 3.54422133E-02 1.80246157E-01 ERR 11 2.46299647E-04 3.70102675E-02 2.01262013E-01 ERR 12 3.57654922E-04 3.93143970E-02 2.20533745E-01 ERR 14 1.95974374E-04 4.31813231E-02 2.44026531E-01 ERR 15 4.94080997E-04 4.73774930E-02 2.74308998E-01 ERR 16 9.62150633E-04 5.07005683E-02 3.17482301E-01 ERR 18 1.31336233E-03 5.39222716E-02 3.25526268E-01 ERR 21 1.07020831E-03 5.67663475E-02 3.04995023E-01 ERR 24 9.88977484E-04 6.37987961E-02 3.83686143E-01 ERR 26 9.63361971E-04 6.81972633E-02 4.92021943E-01 ERR 29 1.81730719E-03 7.47758864E-02 6.38563225E-01