This section describes the core usage of the colabfit-tools package.

Parsing data

Properties can be added into the Database is by creating or loading a Configuration object, attaching the Property data to the info and/or arrays dictionaries of the Configuration (see here for more details), then using the insert_data() method.

In addition to the Configuration with attached Property information, insert_data() will also require a property map describing how to map the attached Property information onto an existing property definition (see Property definitions).

A property map should have the following structure:

    <property_name>: {
        <property_field>: {
            'field': <key_for_info_or_arrays>,
            'units': <ase_readable_units>

See below for the definitions of each of the above keys/values:

Note that insert_data() will attempt to load every Property specified in property_map for each Configuration. This means that if there are P properties in property_map and C Configurations, a maximum of P*C Properties will be loaded in total. If a Configuration does not have the necessary data for loading a given Property, that Property is skipped for the given Configuration and a warning is raised.

Detecting duplicates

All entities in the Database (Configuration, Property, PropertySetting, ConfigurationSet, and Dataset) are stored with a unique ID that is generated by hashing the corresponding entity (see the documentation for their respective hash functions for more detail). Because of this, if two entities are identical, they will only be stored once in the Database. This can be useful when trying to remove duplicate data in a Dataset, as it ensures that no duplicates are added during insert_data().

For example, this enables a user to take advantage of the Mongo count_documents() function to see if a Configuration is linked to more than one Property

    {'': {'$exists': True}}

Similarly, the 'relationships' fields of entities can be used to check if two Datasets have any common Configurations.

Synchronizing a Dataset

When working with a Dataset, it is important to make sure that the dataset has been “synchronized” in order to ensure that all of the data (configuration labels, configuration sets, aggregated metadata, …) have been properly updated to reflect any recent changes to the Dataset.

There are three points in the Database where data aggregation is performed (and thus where care must be taken to ensure synchronization):

Synchronization can be performed by using the resync=True argument when calling get_configuration_set() and get_dataset(). Aggregation is automatically performed when inserting ConfigurationSets. Aggregation is not automatically performed when inserting Datasets in order to avoid re-aggregating ConfigurationSet information unnecessarily; therefore resync=True may also be used for insert_dataset().

In order to re-synchronize the entities without using the get_*() methods, call the aggregate_*() methods directly. A common scenario where this may be necessary is when using apply_labels() in order to make sure that the changes are reflected in the ConfigurationSets and Datasets.

Applying configuration labels

Configuration labels should be applied using the apply_labels() method.

An example configuration_label_regexes:

    query={'nsites': {'$lt': 100}},

See the Si PRX GAP tutorial for a more complete example.

Building configuration sets

There are two steps to building a ConfigurationSet:

First, extracting the IDs of the Configurations that should be included in the ConfigurationSet:

co_ids = client.get_data(
    query={'_id': {'$in': <all_co_ids_in_dataset>}, 'nsites': {'$lt': 100}},

And second, calling insert_configuration_set():

cs_id = client.insert_configuration_set(
    description='Configurations with fewer than 100 atoms'

Note that in the first step, '_id': {'$in': <all_co_ids_in_dataset>} is used in order to limit the query to include only the Configurations within a given Dataset, rather than all of the Configurations in the entire Database.

See the Si PRX GAP tutorial for a more complete example.

Attaching property settings

A PropertySettings object can be attached to a Property by specifying the property_settings argument in insert_data().

pso = PropertySettings(
    description='A basic VASP calculation',
    labels=['PBE', 'GGA'],

        <property_name>: pso,

Data exploration

The get_data(), plot_histograms(), and get_statistics() functions can be extremely useful for quickly visualizing your data and detecting outliers.

energies = client.get_data('properties', '<property_name>.energy', ravel=True)
forces   = client.get_data('properties', '<property_name>.forces', concatenate=True)
# From the QM9 example

            ['qm9-property.a', 'qm9-property.b', 'qm9-property.c'],

            ['qm9-property.a', 'qm9-property.b', 'qm9-property.c',],

See the QM9 example and the Si PRX GAP example to further explore the benefits of these functions.

Since Configurations inherit from ase.Atoms objects, they work seamlessly with ASE’s visualization tools.

# Run inside of a Jupyter Notebook

configurations = client.get_configurations(configuration_ids)

from ase.visualize import view

# Creates a Jupyter Widget; may require `pip install nglview` first
view(configurations, viewer='nglview')

Filtering a Dataset

Datasets can be easily filtered to remove unwanted entries or extract subsets of interest. Filtering can be done using the filter_on_properties() or filter_on_configurations() methods.

# From the QM9 example

clean_config_sets, clean_property_ids = client.filter_on_properties(
    filter_fxn=lambda x: (x['qm9-property']['a']['source-value'] < 20)
        and x['qm9-property']['b']['source-value'] < 10,
    fields=['qm9-property.a.source-value', 'qm9-property.b.source-value'],

Note the use of the ds_id argument, which makes sure that the returned ConfigurationSet IDs and Property IDs are only those that are contained within the given Dataset.

Data transformations

It is often necessary to transform the data in a Dataset in order to improve performance when fitting models to the data, or to convert the data into a different format. This can be done using the transfom argument of the insert_data() function. The transform argument should be a callable function that modifies the Configuration in-place:

def per_atom(c):['energy'] /= len(c)


Supported file formats

Ideally, raw data should be stored in Extended XYZ format. This is the default format used by colabfit-tools, and should be suitable for almost all use cases. CFG files (used by Moment Tensor Potentials) are also supported, but are not recommended.

Data that is in a custom format (e.g., JSON, HDF5, …) that cannot be easily read by will require the use of a FolderConverter instance, which needs to be supplied with a custom reader() function for parsing the data.