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6.82 kB
| """Set up MLIP infrastructure for high-throughput migration barrier computation. | |
| Installs and validates MLIP tools for nudged elastic band (NEB) calculations: | |
| - CHGNet: universal crystal Hamiltonian Graph neural Network | |
| - MACE-MP-0: MACE architecture trained on Materials Project trajectories | |
| - M3GNet: universal potential from Materials Project | |
| - Orb-v3: Orbital-based MLIP | |
| This script: | |
| 1. Checks what's installed | |
| 2. Attempts installation of missing packages | |
| 3. Validates each potential on a test structure | |
| 4. Generates a configuration file for the NEB pipeline | |
| Usage: | |
| python scripts/setup_mlip_infrastructure.py | |
| python scripts/setup_mlip_infrastructure.py --check-only | |
| python scripts/setup_mlip_infrastructure.py --install | |
| """ | |
| import argparse, os, sys, subprocess, json, warnings | |
| from pathlib import Path | |
| MLIP_PACKAGES = { | |
| "chgnet": "chgnet", | |
| "mace": "mace-torch", | |
| "matgl": "matgl", | |
| "orb": "orb-models", | |
| } | |
| TEST_STRUCTURE = """ | |
| { | |
| "@module": "pymatgen.core.structure", | |
| "@class": "Structure", | |
| "lattice": {"matrix": [[3.0, 0.0, 0.0], [0.0, 3.0, 0.0], [0.0, 0.0, 3.0]], "pbc": [true, true, true]}, | |
| "sites": [ | |
| {"species": [{"element": "Li", "occu": 1}], "abc": [0.0, 0.0, 0.0]}, | |
| {"species": [{"element": "Cl", "occu": 1}], "abc": [0.5, 0.5, 0.5]} | |
| ] | |
| } | |
| """ | |
| def check_installed(): | |
| """Check which MLIP packages are installed.""" | |
| results = {} | |
| for name, pkg in MLIP_PACKAGES.items(): | |
| try: | |
| __import__(name.replace("-", "_")) | |
| results[name] = "installed" | |
| except ImportError: | |
| try: | |
| __import__(pkg.replace("-", "_")) | |
| results[name] = "installed" | |
| except ImportError: | |
| results[name] = "not found" | |
| return results | |
| def install_packages(packages): | |
| """Install MLIP packages via pip.""" | |
| for name, pkg in packages.items(): | |
| print(f" Installing {pkg}...") | |
| result = subprocess.run( | |
| [sys.executable, "-m", "pip", "install", pkg], | |
| capture_output=True, text=True | |
| ) | |
| if result.returncode == 0: | |
| print(f" {name}: installed") | |
| else: | |
| print(f" {name}: failed — {result.stderr[-200:]}") | |
| def validate_chgnet(structure_dict): | |
| """Validate CHGNet can predict on test structure.""" | |
| import json | |
| from pymatgen.core import Structure | |
| from chgnet.model import CHGNet | |
| from chgnet.utils import write_structures_to_POSCAR | |
| struct = Structure.from_dict(structure_dict) | |
| model = CHGNet.load() | |
| prediction = model.predict_structure(struct) | |
| return { | |
| "energy": float(prediction["e"]), | |
| "forces_shape": list(prediction["f"].shape), | |
| } | |
| def validate_mace(structure_dict): | |
| """Validate MACE can predict on test structure.""" | |
| import torch | |
| from mace.calculators import MACECalculator | |
| from ase.io import read | |
| from pymatgen.core import Structure | |
| from pymatgen.io.ase import AseAtomsAdaptor | |
| struct = Structure.from_dict(structure_dict) | |
| atoms = AseAtomsAdaptor.get_atoms(struct) | |
| calc = MACECalculator(model_path="medium", device="cpu") | |
| atoms.set_calculator(calc) | |
| energy = atoms.get_potential_energy() | |
| forces = atoms.get_forces() | |
| return { | |
| "energy": float(energy), | |
| "forces_shape": list(forces.shape), | |
| } | |
| def main(): | |
| parser = argparse.ArgumentParser(description="MLIP infrastructure setup") | |
| parser.add_argument("--check-only", action="store_true", | |
| help="Check installed packages only") | |
| parser.add_argument("--install", action="store_true", | |
| help="Install missing MLIP packages") | |
| parser.add_argument("--validate", action="store_true", | |
| help="Validate installed potentials on test structure") | |
| args = parser.parse_args() | |
| BASE_DIR = Path(__file__).resolve().parent.parent | |
| print("=" * 60) | |
| print(" MLIP INFRASTRUCTURE SETUP") | |
| print(" High-throughput migration barrier computation pipeline") | |
| print("=" * 60) | |
| # Check installed packages | |
| print("\n Checking installed MLIP packages...") | |
| installed = check_installed() | |
| for name, status in installed.items(): | |
| print(f" {name:12s}: {status}") | |
| if args.install: | |
| to_install = {k: v for k, v in MLIP_PACKAGES.items() if installed[k] == "not found"} | |
| if to_install: | |
| print(f"\n Installing {len(to_install)} packages...") | |
| install_packages(to_install) | |
| else: | |
| print("\n All packages already installed.") | |
| if args.validate: | |
| print("\n Validating potentials...") | |
| struct_dict = json.loads(TEST_STRUCTURE) | |
| if installed.get("chgnet") == "installed": | |
| try: | |
| result = validate_chgnet(struct_dict) | |
| print(f" CHGNet: OK (energy={result['energy']:.3f} eV)") | |
| except Exception as e: | |
| print(f" CHGNet: validation failed — {str(e)[:80]}") | |
| if installed.get("mace") == "installed": | |
| try: | |
| result = validate_mace(struct_dict) | |
| print(f" MACE: OK (energy={result['energy']:.3f} eV)") | |
| except Exception as e: | |
| print(f" MACE: validation failed — {str(e)[:80]}") | |
| # Generate config file | |
| if not args.check_only: | |
| config = { | |
| "potentials": installed, | |
| "pipeline": { | |
| "bvse_barrier_threshold": 0.5, | |
| "mlip_neb_grid": [5, 5, 5], | |
| "mlip_neb_spring_constant": 5.0, | |
| "mlip_neb_fmax": 0.05, | |
| "mlip_neb_steps": 500, | |
| }, | |
| "target_subset": "gold_battery_li", | |
| "description": "Li-containing Gold-tier battery-family entries", | |
| } | |
| config_path = BASE_DIR / "configs" / "mlip_pipeline.json" | |
| print(f"\n Writing config to {config_path}...") | |
| config_path.parent.mkdir(parents=True, exist_ok=True) | |
| with open(config_path, "w") as f: | |
| json.dump(config, f, indent=2) | |
| # Print next steps | |
| print(f"\n{'─' * 60}") | |
| print(" NEXT STEPS") | |
| print(f" {'─' * 60}") | |
| print(""" | |
| 1. Install MLIP packages: | |
| pip install chgnet mace-torch matgl orb-models | |
| 2. Run BVSE pre-filter on Li/Na entries: | |
| python scripts/compute_bvse_barriers.py --subset gold --limit 50000 | |
| 3. Run MLIP-NEB on BVSE-filtered subset: | |
| python scripts/run_mlip_neb_pipeline.py --input dataset/bvse_filtered.json | |
| 4. Update sse_candidate_score with full 5 gates: | |
| python scripts/compute_sse_candidate_score.py | |
| """) | |
| print("=" * 60) | |
| if __name__ == "__main__": | |
| main() | |