AutoPot Workflow
================

Presently, AutoPot consists of one workflow that can do a pre-selection of
training configurations from a large set of training candidates using
``select``, and/or a selection of training configurations during MD simulations
using ``md_select``.

After each selection round, selected configurations are automatically
calculated with ``sp_calc`` and the potential is automatically re-trained using
``train``, taking into account the newly calculated configurations. This
process of selecting, calculating, and re-training is repeated until no
uncertain configurations are found anymore.

.. image:: ../flow_chart_subworkflows.png
   :alt: AutoPot workflow subworkflows
   :align: center

The three stages can be called standalone or in sequence from the orchestrator.
They are always called in the order ``calculate_ts`` ->
``select_and_calc_ts`` -> ``md_select_and_calc_ts``.

The behavior of the orchestrator can be controlled by the following arguments:

.. code-block:: text

   usage: motoko orchestrator start [-h] [--detach] [--wait WAIT] --run_name RUN_NAME --potential 08.mtp [--fresh_potential 08.mtp] --training_set ts.cfg [--calculate_ts]
                                    [--atom_types W=0,Ta=1,...] [--select] [--grades [10000 1000 100 ...]] [--training_candidate_set tcs.cfg] [--md_select]
                                    [--md_configurations configurations.xyz]

   options:
     -h, --help            show this help message and exit
     --detach, -d          For starting: run in detach/daemon mode with Zdaemon manager
     --wait WAIT           Waiting time between state checks in seconds
     --run_name RUN_NAME, -n RUN_NAME
                           run_name to use for all produced jobs
     --potential 08.mtp, -p 08.mtp
                           interatomic potential to start with
     --fresh_potential 08.mtp, -P 08.mtp
                           untrained interatomic potential to be used for training from scratch after collecting all training configurations
     --training_set ts.cfg, -t ts.cfg
                           initial training set
     --calculate_ts, -c    calculate the initial training set and train the potential on it
     --atom_types W=0,Ta=1,..., -a W=0,Ta=1,...
                           mapping of atomic types to numbers according to convention used in the potential code
     --select, -s          pre-select configurations from the candidate set
     --grades [10000 1000 100 ...], -g [10000 1000 100 ...]
                           Maximum extrapolation grades above which iteratively configurations are selected for training
     --training_candidate_set tcs.cfg, -T tcs.cfg
                           Set with the training candidates
     --md_select, -S       select configurations from MD simulations
     --md_configurations configurations.xyz, -C configurations.xyz
                           file with atomic configurations on which to run the MD simulations. If not given, run MD on each configuration from the training set