{ "cells": [ { "cell_type": "markdown", "id": "e7b5f7ac-9860-48c8-b1b8-e2d3f4b7b8ab", "metadata": {}, "source": [ "## Load the Simple Active Learning job from disk.\n", "\n", "``retrained_params_job`` is the best ParAMS training job that was done during the Active Learning." ] }, { "cell_type": "code", "execution_count": 1, "id": "e643c1e4-4827-4af5-b0dd-7c2861825c0f", "metadata": {}, "outputs": [], "source": [ "from scm.simple_active_learning import SimpleActiveLearningJob\n", "from scm.params.plams.paramsjob import ParAMSJob\n", "from scm.params import ResultsImporter, NoParameters\n", "import scm.plams as plams\n", "import os\n", "\n", "# replace the path with your own path !\n", "previous_sal_job_path = os.path.expandvars(\"$AMSHOME/examples/SAL/Output/SingleMolecule/plams_workdir/sal\")\n", "sal_job = SimpleActiveLearningJob.load_external(previous_sal_job_path)\n", "retrained_params_job = sal_job.results.get_params_job()" ] }, { "cell_type": "markdown", "id": "69d2d982-fa6e-4a46-b762-3851691d2722", "metadata": {}, "source": [ "## Get results for M3GNet-UP-2022 universal potential\n", "\n", "Are the retrained results any better than those from the M3GNet-UP-2022 universal potential?\n", "\n", "To find out, we need to evaluate the training and validation sets also with M3GNet-UP-2022. This can be done with the ParAMS \"SinglePoint\" task, which does not perform any parameter optimization but instead evaluates the training and validation sets with a given engine.\n", "\n", "To set the engine settings, we need to call the ``set_extra_engine()`` method on the job collection and then store the results in a new folder that can be read by the new ParAMSJob. The easiest way to achieve this is to use the ``ResultsImporter`` class, even though we do not import any new results. When running the SinglePoint, we also have to explicitly specify that there are no parameters and store the ``NoParameters`` parameter interface:" ] }, { "cell_type": "code", "execution_count": 4, "id": "ea1e9cba-9e5a-4a11-9a26-b597110810d5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "Jobs: job_collection.yaml.gz\n", "dtype: EngineCollection\n", "version: '2024.101'\n", "---\n", "ID: 'ParAMS'\n", "AMSInput: |\n", " Engine MLPotential\n", " Model M3GNet-UP-2022\n", " EndEngine\n", "---\n", "ID: 'forcefield;;type;UFF;'\n", "AMSInput: |\n", " Engine forcefield\n", " type UFF\n", " EndEngine\n", "...\n", "\n" ] } ], "source": [ "m3gnet_up_s = plams.Settings()\n", "m3gnet_up_s.input.MLPotential.Model = \"M3GNet-UP-2022\"\n", "ri = ResultsImporter.from_params_results(retrained_params_job.results)\n", "ri.job_collection.set_extra_engine(m3gnet_up_s)\n", "print(ri.job_collection.engines)" ] }, { "cell_type": "code", "execution_count": 5, "id": "f59baba8-aa5a-4f8a-a234-542798259a89", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['results_importer_settings.yaml',\n", " 'validation_set.yaml',\n", " 'job_collection_engines.yaml',\n", " 'training_set.yaml',\n", " 'parameter_interface.yaml',\n", " 'job_collection.yaml']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pi = NoParameters()\n", "folder = \"m3gnet-up-data\"\n", "ri.store(folder, backup=False) # will create the directory\n", "pi.yaml_store(f\"{folder}/parameter_interface.yaml\")\n", "os.listdir(folder)" ] }, { "cell_type": "markdown", "id": "c516e94c-3ce6-4095-9c93-8e7ea223cb93", "metadata": {}, "source": [ "We can now **initialize the new ParAMS SinglePoint job and run it**:" ] }, { "cell_type": "code", "execution_count": 6, "id": "3d30a3b2-8b66-483a-9eba-ca395120e930", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PLAMS working folder: /home/hellstrom/adfhome/userdoc/Workflows/SimpleActiveLearning/PythonExamples/SALSingleMoleculeCompareToM3GNetUP2022/plams_workdir_singlepoint_validation\n", "[12.02|15:31:23] JOB m3gnet-up STARTED\n", "[12.02|15:31:23] JOB m3gnet-up RUNNING\n", "[12.02|15:31:39] JOB m3gnet-up FINISHED\n", "[12.02|15:31:39] JOB m3gnet-up SUCCESSFUL\n" ] } ], "source": [ "new_params_job = ParAMSJob.from_yaml(folder)\n", "new_params_job.settings.input.Task = \"SinglePoint\"\n", "new_params_job.name = \"m3gnet-up\"\n", "\n", "plams.init(folder=\"plams_workdir_singlepoint_validation\")\n", "new_params_job.run();" ] }, { "cell_type": "markdown", "id": "1028b213-aa7f-470b-afbd-3c9a800ad7a1", "metadata": {}, "source": [ "### M3GNet-UP-2022 (predicted) forces vs. the reference (here UFF) forces" ] }, { "cell_type": "code", "execution_count": 7, "id": "cfbfff2b-fac1-4e71-aafb-75ea2cab6f4b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "new_params_job.results.plot_simple_correlation(\"forces\", source=\"best\", title=\"M3GNet-UP-2022\");" ] }, { "cell_type": "markdown", "id": "af54994e-cb47-4bf7-bb5f-862249f32ca7", "metadata": {}, "source": [ "Here we can see that M3GNet-UP-2022 gives quite different force prediction compared to our chosen reference method (UFF force field).\n", "\n", "Note that M3GNet-UP-2022 was trained to PBE DFT data, and the plot above shows the agreement to the UFF force field. The plot does not show the agreement to the PBE level of theory to which M3GNet-UP-2022 was originally trained!" ] }, { "cell_type": "markdown", "id": "ff875480-55a2-4515-81e9-337df52aa71c", "metadata": {}, "source": [ "### Retrained M3GNet (predicted) forces vs. the reference (here UFF) forces" ] }, { "cell_type": "code", "execution_count": 8, "id": "9a038c54-1384-4272-922a-7b5b9c69c03d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "retrained_params_job.results.plot_simple_correlation(\"forces\", source=\"best\", title=\"Retrained M3GNet\");" ] }, { "cell_type": "markdown", "id": "d21abaf4-c2c6-442f-af66-0e05b9737e69", "metadata": {}, "source": [ "This is the same plot as shown in the previous tutorial. We can see that the active learning retraining has led to significant improvements in reproducing the reference data!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }