# Iterative DFT vs MLIP benchmark workflow for phonons We will not perform VASP calculations in realtime for this tutorial, but rather mock vasp runs. Thus, it is necessary to set folders with pre-computed VASP output files for execution in the notebook. ```python # Please note that I am reusing the same relaxations here in several steps. # This is only to save storage on our repo. It has influence on the result. ref_paths = { "tight relax 1_mp-117_0.94_pre1": "tutorial_data/tight_relax_1_mp-117_0.94_0_42", "tight relax 1_mp-117_0.94_0": "tutorial_data/tight_relax_1_mp-117_0.94_0_42", "tight relax 1_mp-117_0.94_1": "tutorial_data/tight_relax_1_mp-117_0.94_0_42", "dft tight relax 1_mp-117_0.94_0": "tutorial_data/tight_relax_1_mp-117_0.94_0_42", "tight relax 2_mp-117_0.94_pre1": "tutorial_data/tight_relax_2_mp-117_0.94_0_43", "tight relax 2_mp-117_0.94_0": "tutorial_data/tight_relax_2_mp-117_0.94_0_43", "tight relax 2_mp-117_0.94_1": "tutorial_data/tight_relax_2_mp-117_0.94_0_43", "dft tight relax 2_mp-117_0.94_0": "tutorial_data/tight_relax_2_mp-117_0.94_0_43", "dft static_mp-117_0.94_0": "tutorial_data/tight_relax_2_mp-117_0.94_0_43", "tight relax 1_mp-117_1.0_pre1": "tutorial_data/tight_relax_1_mp-117_1.0_0_47", "tight relax 1_mp-117_1.0_0": "tutorial_data/tight_relax_1_mp-117_1.0_0_47", "tight relax 1_mp-117_1.0_1": "tutorial_data/tight_relax_1_mp-117_1.0_0_47", "dft tight relax 1_mp-117_1.0_0": "tutorial_data/tight_relax_1_mp-117_1.0_0_47", "dft tight relax 1_mp-117_1.0_1": "tutorial_data/tight_relax_1_mp-117_1.0_0_47", "tight relax 2_mp-117_1.0_pre1": "tutorial_data/tight_relax_2_mp-117_1.0_0_48", "tight relax 2_mp-117_1.0_0": "tutorial_data/tight_relax_2_mp-117_1.0_0_48", "tight relax 2_mp-117_1.0_1": "tutorial_data/tight_relax_2_mp-117_1.0_0_48", "dft tight relax 2_mp-117_1.0_0": "tutorial_data/tight_relax_2_mp-117_1.0_0_48", "dft tight relax 2_mp-117_1.0_1": "tutorial_data/tight_relax_2_mp-117_1.0_0_48", "dft static_mp-117_1.0_0": "tutorial_data/tight_relax_2_mp-117_1.0_0_48", "tight relax 1_mp-117_1.06_pre1": "tutorial_data/tight_relax_1_mp-117_1.06_0_52", "tight relax 1_mp-117_1.06_0": "tutorial_data/tight_relax_1_mp-117_1.06_0_52", "tight relax 1_mp-117_1.06_1": "tutorial_data/tight_relax_1_mp-117_1.06_0_52", "dft tight relax 1_mp-117_1.06_0": "tutorial_data/tight_relax_1_mp-117_1.06_0_52", "dft tight relax 1_mp-117_1.06_1": "tutorial_data/tight_relax_1_mp-117_1.06_0_52", "tight relax 2_mp-117_1.06_pre1": "tutorial_data/tight_relax_2_mp-117_1.06_0_53", "tight relax 2_mp-117_1.06_0": "tutorial_data/tight_relax_2_mp-117_1.06_0_53", "tight relax 2_mp-117_1.06_1": "tutorial_data/tight_relax_2_mp-117_1.06_0_53", "dft tight relax 2_mp-117_1.06_0": "tutorial_data/tight_relax_2_mp-117_1.06_0_53", "dft static_mp-117_1.06_0": "tutorial_data/tight_relax_2_mp-117_1.06_0_53", "Sn-stat_iso_atom_0": "tutorial_data/Sn-stat_iso_atom_0_25", "Sn-stat_iso_atom_1": "tutorial_data/Sn-stat_iso_atom_0_25", "dft rattle static 1/1_mp-117_0.94_0": "tutorial_data/dft_rattle_static_1_1_mp-117_0.94_0_63", "dft rattle static 1/1_mp-117_0.94_1": "tutorial_data/dft_rattle_static_1_1_mp-117_0.94_1_257", "dft rattle static 1/1_mp-117_1.0_0": "tutorial_data/dft_rattle_static_1_1_mp-117_1.0_0_65", "dft rattle static 1/1_mp-117_1.0_1": "tutorial_data/dft_rattle_static_1_1_mp-117_1.0_1_255", "dft rattle static 1/1_mp-117_1.06_0": "tutorial_data/dft_rattle_static_1_1_mp-117_1.06_0_67", "dft rattle static 1/1_mp-117_1.06_1": "tutorial_data/dft_rattle_static_1_1_mp-117_1.06_1_259", "dft phonon static 1/1_mp-117_0.94_0": "tutorial_data/dft_phonon_static_1_1_mp-117_0.94_0_193", "dft phonon static 1/1_mp-117_1.0_0": "tutorial_data/dft_phonon_static_1_1_mp-117_1.0_0_189", "dft phonon static 1/1_mp-117_1.06_0": "tutorial_data/dft_phonon_static_1_1_mp-117_1.06_0_191", } ``` ```python import warnings from atomate2.vasp.flows.core import DoubleRelaxMaker from atomate2.vasp.jobs.core import StaticMaker, TightRelaxMaker from atomate2.vasp.jobs.phonons import PhononDisplacementMaker from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator from jobflow import Flow, run_locally from mock_vasp import TEST_DIR, mock_vasp from pymatgen.core.structure import Structure from autoplex.auto.phonons.flows import ( CompleteDFTvsMLBenchmarkWorkflow, IterativeCompleteDFTvsMLBenchmarkWorkflow, ) warnings.filterwarnings("ignore") ``` First, we define all relevant Makers for the workflow, used to train and finetune ML potentials for phonons. We will pre-relax the structures before starting the workflow. We will now define the relax maker, a displacement maker (static maker), static energy maker and a static energy maker for isolated atoms. ```python phonon_bulk_relax_maker = DoubleRelaxMaker.from_relax_maker( TightRelaxMaker( run_vasp_kwargs={"handlers": ()}, input_set_generator=TightRelaxSetGenerator( user_incar_settings={ "GGA": "PE", "ISPIN": 1, "KSPACING": 0.1, "ALGO": "Normal", "LAECHG": False, "ISMEAR": 1, "ENCUT": 700, "IBRION": 1, "ISYM": 0, "SIGMA": 0.05, "LCHARG": False, "LWAVE": False, "LVTOT": False, "LORBIT": None, "LOPTICS": False, "LREAL": False, "ISIF": 4, "NPAR": 4, } ), ) ) ``` ```python phonon_displacement_maker = PhononDisplacementMaker( name="dft phonon static", run_vasp_kwargs={"handlers": ()}, input_set_generator=StaticSetGenerator( user_incar_settings={ "GGA": "PE", "IBRION": -1, "ISPIN": 1, "ISMEAR": 1, "ISIF": 3, "ENCUT": 700, "EDIFF": 1e-7, "LAECHG": False, "LREAL": False, "ALGO": "Normal", "NSW": 0, "LCHARG": False, "LWAVE": False, "LVTOT": False, "LORBIT": None, "LOPTICS": False, "SIGMA": 0.05, "ISYM": 0, "KSPACING": 0.1, "NPAR": 4, }, auto_ispin=False, ), ) ``` ```python phonon_static_energy_maker = phonon_displacement_maker static_isolated_atom_maker = StaticMaker( run_vasp_kwargs={"handlers": ()}, input_set_generator=StaticSetGenerator( user_kpoints_settings={"reciprocal_density": 1}, user_incar_settings={ "GGA": "PE", "ALGO": "Normal", "ISPIN": 1, "LAECHG": False, "ISMEAR": 0, "LCHARG": False, "LWAVE": False, "LVTOT": False, "LORBIT": None, "LOPTICS": False, "NPAR": 4, }, ), ) ``` First, collect a number of structures and then optimize them in advance of the workflow. One can also perform subsequent optimizations with different k-point settings, for example. ```python job_list = [] structure_list = [] benchmark_structure_list = [] start_mpids = ["mp-117"] start_poscars = [TEST_DIR / "tutorial_data/POSCAR-mp-117"] mpids = [] for mpid, start_poscar in zip(start_mpids, start_poscars): for scale in [0.94, 1.0, 1.06]: structure = Structure.from_file(start_poscar) volume = structure.copy().volume structure = structure.scale_lattice((scale**3) * volume) # added the cube job_opt = phonon_bulk_relax_maker.make(structure) job_opt.append_name("_" + mpid + "_" + str(scale) + "_pre1") job_list.append(job_opt) structure_list.append(job_opt.output.structure) mpids.append(mpid + "_" + str(scale)) mpbenchmark = mpids benchmark_structure_list = structure_list ``` ```python iteration_flow = IterativeCompleteDFTvsMLBenchmarkWorkflow( max_iterations=2, # with the current test data, you can switch between 1 and 2 rms_max=0.2, complete_dft_vs_ml_benchmark_workflow_0=CompleteDFTvsMLBenchmarkWorkflow( symprec=1e-3, apply_data_preprocessing=True, add_dft_rattled_struct=True, add_dft_phonon_struct=True, volume_custom_scale_factors=[1.0], rattle_type=0, distort_type=0, rattle_std=0.1, # benchmark_kwargs={"relax_maker_kwargs": {"relax_cell": False}}, supercell_settings={ "min_length": 10, "max_length": 15, "min_atoms": 10, "max_atoms": 300, "fallback_min_length": 9, }, # settings that worked with a GAP split_ratio=0.33, regularization=False, separated=False, num_processes_fit=48, displacement_maker=phonon_displacement_maker, phonon_bulk_relax_maker=phonon_bulk_relax_maker, phonon_static_energy_maker=phonon_static_energy_maker, rattled_bulk_relax_maker=phonon_bulk_relax_maker, isolated_atom_maker=static_isolated_atom_maker, ), complete_dft_vs_ml_benchmark_workflow_1=CompleteDFTvsMLBenchmarkWorkflow( symprec=1e-3, apply_data_preprocessing=True, add_dft_phonon_struct=False, add_dft_rattled_struct=True, volume_custom_scale_factors=[1.0], rattle_type=0, distort_type=0, rattle_std=0.1, # maybe 0.1 benchmark_kwargs={"relax_maker_kwargs": {"relax_cell": False}}, supercell_settings={ "min_length": 10, "max_length": 15, "min_atoms": 10, "max_atoms": 300, "fallback_min_length": 9, }, # settings that worked with a GAP split_ratio=0.33, regularization=False, separated=False, num_processes_fit=48, displacement_maker=phonon_displacement_maker, phonon_bulk_relax_maker=phonon_bulk_relax_maker, phonon_static_energy_maker=phonon_static_energy_maker, rattled_bulk_relax_maker=phonon_bulk_relax_maker, isolated_atom_maker=static_isolated_atom_maker, ), ).make( structure_list=structure_list, mp_ids=mpids, benchmark_structures=benchmark_structure_list, benchmark_mp_ids=mpbenchmark, rattle_seed=0, fit_kwargs_list=[ { "soap": { "delta": 1.0, "l_max": 12, "n_max": 10, "atom_sigma": 0.5, "zeta": 4, "cutoff": 5.0, "cutoff_transition_width": 1.0, "central_weight": 1.0, "n_sparse": 6000, "f0": 0.0, "covariance_type": "dot_product", "sparse_method": "cur_points", }, "general": { "two_body": True, "three_body": False, "soap": True, "default_sigma": "{0.001 0.05 0.05 0.0}", "sparse_jitter": 1.0e-8, }, } ], ) job_list.append(iteration_flow) autoplex_flow = Flow(jobs=job_list, output=iteration_flow.output) ``` Now, we are mocking the VASP execution. If you would like leave all folders from the run, set `clean_folders` to False! ```python with mock_vasp(ref_paths=ref_paths, clean_folders=False) as mf: run_locally( autoplex_flow, create_folders=True, ensure_success=True, raise_immediately=True, ) ```