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How to access processed data

The ArchiveQuery allows you to search for entries and access their parsed and processed archive data at the same time. Furthermore, all data is accessible through a convenient Python interface based on the schema rather than plain JSON. See also this guide on using NOMAD's Python schemas to work with processed data.

As a requirement, you have to install the nomad-lab Python package. Follow the How to install nomad-lab guide.

Getting started

To define a query, one can, for example, write

from nomad.client.archive import ArchiveQuery

query = ArchiveQuery(query={}, required={}, page_size=10, results_max=10000)

Although the above query object has an empty query.

The query object is constructed only. To access the desired data, users need to perform two operations manually.

Synchronous Interface


The fetch process is carried out synchronously. Users can call

# number_of_entries = query.fetch(1000) # fetch 1000 entries
number_of_entries = query.fetch()  # fetch at most results_max entries

to perform the fetch process to fetch up to results_max entries. An indicative number n can be provided fetch(n) . Given that each upload may contain various numbers of entries, the fetch process guarantees at least n entries will be fetched. The exact number is determined by page_size, indicating how many uploads in each page. However, this would be limited to the results_max. The exact qualified number of entries will be returned. Meanwhile, the qualified upload list would be populated with their IDs. To check all qualified upload IDs, one can call upload_list() method to return the full list.


If applicable, it is possible to fetch a large number of entries first and then perform a second fetch by using some upload ID in the first fetch result as the after argument so that some middle segment can be downloaded.


After fetching the qualified uploads, the desired data can be downloaded asynchronously. One can call

# results = # download 1000 entries
results =  # download all fetched entries if fetched otherwise fetch and download up to `results_max` entries

to download up to results_max entries. The downloaded results are returned as a list. Alternatively, it is possible to just download a portion of previously fetched entries at a single time. For example,

# previously fetched for example 1000 entries
# but only download the first 100 (approx.) entries
results =

The same download(n) method can be called repeatedly. If there are no sufficient entries, new entries will be automatically fetched. If there are no more entries, the returned result list is empty. For example,

total_results = []
while True:
    result =
    if len(result) == 0:

There is no retry mechanism in the download process. If any uploads fail to be downloaded due to server error, it is kept in the list otherwise removed.

Asynchronous Interface

Some applications, such as Jupyter Notebook, may run a global/top level event loop. To query data in those environments, one can use the asynchronous interface.

number_of_entries = await query.async_fetch()  # indicative number n applies: async_fetch(n)
results = await query.async_download()  # indicative number n applies: async_download(n)

Alternatively, if one wants to use the asynchronous interface, it is necessary to patch the global event loop to allow nested loops.

To do so, one can add the following at the beginning of the notebook.

import nest_asyncio


A Complete Rundown

Here we show a valid query and acquire data from server.

We first define the desired query and construct the object. We limit the maximum number of entries to be 10000 and 10 uploads per page.

from nomad.client.archive import ArchiveQuery

required = {
    'workflow': {
        'calculation_result_ref': {
            'energy': '*',
            'system_ref': {
                'chemical_composition_reduced': '*'

query = {
    'results.method.simulation.program_name': 'VASP',
    'results.material.elements': ['Ti']

query = ArchiveQuery(query=query, required=required, page_size=10, results_max=10000)

Let's fetch some entries.


If we print the upload list, it would be

[('-19NlAwxTCCXb6YT9Plifw', 526), ('-2ewONNGTZ68zuTQ6zrRZw', 4), ('-3LrFBvFQtCtmEp3Hy15EA', 12), ('-3ofEqLvSZiqo59vtf-TAQ', 4), ('-4W-jogwReafpdva4ELdrw', 32), ('-BLVfvlJRWawtHyuUWvP_g', 68), ('-Dm30DqRQX6pZUbJYwHUmw', 320), ('-Jfjp-lZSjqyaph2chqZfw', 6), ('-K2QS7s4QiqRg6nMPqzaTw', 82), ('-Li36ZXhQPucJvkd8yzYoA', 10)]

So upload -19NlAwxTCCXb6YT9Plifw has 526 qualified entries, upload -2ewONNGTZ68zuTQ6zrRZw has 4 qualified entries, and so on. The summation of the above entries gives 1064 entries in total as shown in terminal message.

Now data can be downloaded.

result =
print(f'Downloaded {len(result)} entries.')  # Downloaded 526 entries.

Since the first upload has 526 entries, they will be downloaded in this call. The list would have the first upload removed as it has been downloaded.

[('-2ewONNGTZ68zuTQ6zrRZw', 4), ('-3LrFBvFQtCtmEp3Hy15EA', 12), ('-3ofEqLvSZiqo59vtf-TAQ', 4), ('-4W-jogwReafpdva4ELdrw', 32), ('-BLVfvlJRWawtHyuUWvP_g', 68), ('-Dm30DqRQX6pZUbJYwHUmw', 320), ('-Jfjp-lZSjqyaph2chqZfw', 6), ('-K2QS7s4QiqRg6nMPqzaTw', 82), ('-Li36ZXhQPucJvkd8yzYoA', 10)]

It is possible to download more data.

result =
print(f'Downloaded {len(result)} entries.')  # Downloaded 440 entries.

The first six uploads will be downloaded to meet the required (at least) 300 entries, which total to 440 entries. What's left in the list would be

[('-Jfjp-lZSjqyaph2chqZfw', 6), ('-K2QS7s4QiqRg6nMPqzaTw', 82), ('-Li36ZXhQPucJvkd8yzYoA', 10)]

We perform one more download call to illustrate that fetch process will be automatically performed.

result =
print(f'Downloaded {len(result)} entries.')  # Downloaded 102 entries.

In the above, we request additional 100 entries, however, the list contains only 6+82+10=98 entries, fetch process will be called to fetch new entries from server. You will see the following message in terminal.

Fetching remote uploads...
787 entries are qualified and added to the download list.
Downloading required data...
Downloaded 102 entries.
[('-NiRWNGjS--JtFoEnYrCfg', 8), ('-OcPUKZtS6u3lXlkWBM4qg', 129), ('-PA35e2ZRsq4AdDfBU4M_g', 14), ('-TG77dGiSTyrDAFNqTKa6Q', 366), ('-VzlPYtnS4q1tSl3NOmlCw', 178), ('-XeqzVqwSMCJFwhvDqWs8A', 14), ('-Y7gwnleQI6Q61jp024fXQ', 16), ('-Zf1RO1MQXegYVTbFybtQQ', 8), ('-Zm4S9VGRdOX-kbF1J5lOA', 50)]

Argument List

The following arguments are acceptable for ArchiveQuery.

  • owner : str The scope of data to access. Default: 'visible'
  • query : dict The API query. There are no validations of any means carried out by the class, users shall make sure the provided query is valid. Otherwise, server would return error message.
  • required : dict The required quantities.
  • url : str The database url. It can be the one of your local database. The official NOMAD database is used be default if no valid one defined. Default:
  • after : str It can be understood that the data is stored in a sequential list. Each upload has a unique ID, if after is not provided, the query always starts from the first upload. One can choose to query the uploads in the middle of storage by assigning a proper value of after.
  • results_max : int Determine how many entries to download. Note each upload may have multiple entries.
  • page_size : int Page size.
  • username : str Username for authentication.
  • password : str Password for authentication.
  • retry : int In the case of server errors, the fetch process is automatically retried every sleep_time seconds. This argument limits the maximum times of retry.
  • sleep_time : float The interval of fetch retry.

The complete example


This examples uses the new workflow2 workflow system. This is still under development and this example might not yet produce results on the public nomad data.

from nomad.client import ArchiveQuery
from nomad.metainfo import units

query = ArchiveQuery(
        'results.method.simulation.program_name': 'VASP',
        'results.material.elements': ['Ti', 'O'],
        '': {
            'final_energy_difference:lt': 1e-22,
        'workflow2': {
            'results': {
                'calculation_result_ref': {
                    'energy': '*',
                    'system_ref': {
                        'chemical_composition_reduced': '*'

for result in
    calc = None
    if result.workflow2.results:
        calc = result.workflow2.results.calculation_result_ref

    if not calc or not calc.system_ref:

    formula = calc.system_ref.chemical_composition_reduced
        total_energy =
        total_energy = 'N/A'
    print(f'{formula}: {total_energy}')