pymatgen.ext.crystalsai — pymatgen 2020.7.3 documentation

# Source code for pymatgen.ext.crystalsai

    # coding: utf-8
    # Copyright (c) Pymatgen Development Team.
    # Distributed under the terms of the MIT License.

    """
    This module provides an interface to the MaterialsVirtualLab's MEGNet REST API
    for rapid property prediction.
    """

    import requests

    class CrystalAIRester:
        """
        This is a high-level interface to the REST API for http://megnet.crystals.ai
        for property prediction. Using this API, you can use MatErials Graph
        Networks (MEGNet) developed by the Materials Virtual Lab to predict the
        properties of any given crystal. These models are trained on the latest
        versions of the Materials Project. The open-source code
        implementing these MEGNet models are available at
        https://github.com/materialsvirtuallab/megnet.

        For the details of MEGNet and benchmarks, please refer to the following work:
        Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. <i>Graph Networks as a Universal Machine Learning Framework
            for Molecules and Crystals.</i> Chemistry of Materials 2019, acs.chemmater.9b01294.
         DOI: <a href="https://doi.org/10.1021/acs.chemmater.9b01294">10.1021/acs.chemmater.9b01294</a>.</p>
        """

        def __init__(self):
            """
            Init for Rester.
            """
            self.session = requests.Session()
            self.url = "http://megnet.crystals.ai"

        def __enter__(self):
            """
            Support for "with" context.
            """
            return self

        def __exit__(self, exc_type, exc_val, exc_tb):
            """
            Support for "with" context.
            """
            self.session.close()

        def get_available_models(self):
            """
            Returns:
                Available model names. It should be noted that model names starting
                with log10 are for the log10 of that quantity.
            """
            response = self.session.get(self.url + "/models")
            return response.json()

        def predict_mp(self, model_name, mp_id):
            """
            Predict using the http://megnet.crystals.ai API.

            :param model_name: An available model in the
                http://megnet.crystals.ai API. Use get_available_models to find
                the model names.
            :param mp_id: A Materials Project id.
            :return: Predicted value. It should be noted that model names starting
                with log10 are for the log10 of that quantity and you should apply
                10 ** prediction to get the actual value.
            """
            response = None
            url = self.url + "/predict_mp/%s/%s" % (model_name, mp_id)
            try:
                response = self.session.get(url)
                if response.status_code in [200, 400]:
                    return response.json()
                raise ValueError("REST query returned with error status code {}"
                                 .format(response.status_code))
            except Exception as ex:
                msg = "{}. Content: {}".format(str(ex), response.content) \
                    if hasattr(response, "content") else str(ex)
                raise ValueError(msg)

        def predict_structure(self, model_name, structure):
            """
            Predict using the http://megnet.crystals.ai API.

            :param model_name: An available model in the
                http://megnet.crystals.ai API. Use get_available_models to find
                the model names.
            :param structure: A Pymatgen Structure.
            :return: Predicted value. It should be noted that model names starting
                with log10 are for the log10 of that quantity and you should apply
                10 ** prediction to get the actual value.
            """
            response = None
            url = self.url + "/predict_structure/%s" % model_name
            try:
                data = {
                    "structure": structure.to(fmt="POSCAR"),
                    "fmt": "POSCAR"
                }
                response = self.session.post(url, data=data)
                if response.status_code in [200, 400]:
                    return response.json()
                raise ValueError("REST query returned with error status code {}"
                                 .format(response.status_code))
            except Exception as ex:
                msg = "{}. Content: {}".format(str(ex), response.content) \
                    if hasattr(response, "content") else str(ex)
                raise ValueError(msg)