import argparse import sys import pandas as pd import torch from ase import Atoms from tqdm import tqdm import matplotlib.pyplot as plt sys.path.append( "/home/civil/phd/cez218288/Benchmarking/MDBENCHGNN/mdbenchgnn/models/mace" ) from mace.calculators import MACECalculator from ase.optimize import FIRE torch.set_default_dtype(torch.float64) def element_to_atomic_number(element_list): mapping = { "H": 1, "He": 2, "Li": 3, "Be": 4, "B": 5, "C": 6, "N": 7, "O": 8, "F": 9, "Ne": 10, "Na": 11, "Mg": 12, "Al": 13, "Si": 14, "P": 15, "S": 16, "Cl": 17, "Ar": 18, "K": 19, "Ca": 20, "Sc": 21, "Ti": 22, "V": 23, "Cr": 24, "Mn": 25, "Fe": 26, "Co": 27, "Ni": 28, "Cu": 29, "Zn": 30, "Ga": 31, "Ge": 32, "As": 33, "Se": 34, "Br": 35, "Kr": 36, "Rb": 37, "Sr": 38, "Y": 39, "Zr": 40, "Nb": 41, "Mo": 42, "Tc": 43, "Ru": 44, "Rh": 45, "Pd": 46, "Ag": 47, "Cd": 48, "In": 49, "Sn": 50, "Sb": 51, "Te": 52, "I": 53, "Xe": 54, "Cs": 55, "Ba": 56, "La": 57, "Ce": 58, "Pr": 59, "Nd": 60, "Pm": 61, "Sm": 62, "Eu": 63, "Gd": 64, "Tb": 65, "Dy": 66, "Ho": 67, "Er": 68, "Tm": 69, "Yb": 70, "Lu": 71, "Hf": 72, "Ta": 73, "W": 74, "Re": 75, "Os": 76, "Ir": 77, "Pt": 78, "Au": 79, "Hg": 80, "Tl": 81, "Pb": 82, "Bi": 83, "Po": 84, "At": 85, "Rn": 86, "Fr": 87, "Ra": 88, "Ac": 89, "Th": 90, "Pa": 91, "U": 92, "Np": 93, "Pu": 94, "Am": 95, "Cm": 96, "Bk": 97, "Cf": 98, "Es": 99, "Fm": 100, "Md": 101, "No": 102, "Lr": 103, "Rf": 104, "Db": 105, "Sg": 106, "Bh": 107, "Hs": 108, "Mt": 109, "Ds": 110, "Rg": 111, "Cn": 112, "Nh": 113, "Fl": 114, "Mc": 115, "Lv": 116, "Ts": 117, "Og": 118, } atomic_numbers_list = [mapping[element] for element in element_list] # Convert the list of atomic numbers to a tensor atomic_numbers_tensor = torch.tensor(atomic_numbers_list) return atomic_numbers_tensor def map_pbc_to_binary(pbc_list): binary_list = [1 if value else 0 for value in pbc_list] return binary_list def convert_to_ase(data): # Extract data from the input dictionary positions = data["pos"].numpy() cell = data["cell"].numpy() atomic_numbers = data["atomic_numbers"].numpy() pbc = data["pbc"].numpy().astype(bool) # Create an ASE Atoms object atoms = Atoms(positions=positions, numbers=atomic_numbers, cell=cell, pbc=pbc) return atoms def FIRE_Relax(atoms, calculator, NStep=1e4, ftol=1e-5, plot=False): atoms.set_calculator(calculator) optimizer = FIRE(atoms, logfile=None) trajectory = [] energies = [] def record_trajectory(): trajectory.append(atoms.copy()) energies.append(atoms.get_potential_energy()) optimizer.attach(record_trajectory, interval=1) optimizer.run(fmax=ftol, steps=NStep) if plot: # Plotting energy vs. optimization step plt.figure() plt.plot(energies) plt.xlabel("Optimization Step") plt.ylabel("Energy (eV)") plt.title("Energy vs. Optimization Step") plt.grid(True) plt.show() return trajectory, energies # Initialize lists for storing predictions and actual values def initialize_prediction_lists(): return { "Predictions_e": [], "Actuals_e": [], "formation_pred": [], "d_hull_pred": [], "Pred_Energy": [], } # Function to process a data loader def process_data_loader(data, sum_data, limit=None): calculator = MACECalculator( model_path=args.model_path, device=args.device, default_dtype="float64" ) results = initialize_prediction_lists() struc_id = 0 # Add new columns to sum_data sum_data["formation_pred"] = pd.NA sum_data["Pred_Energy"] = pd.NA sum_data["d_hull_pred"] = pd.NA if limit is None: limit = len(data["entries"]) for k in tqdm(range(limit)): d = data["entries"][k]["structure"]["sites"] positions_list = [] atomic_numbers_list = [] for site in d: xyz = site["xyz"] species = site["species"][0] element = species["element"] positions_list.append(xyz) atomic_numbers_list.append(element) positions_tensor = torch.tensor(positions_list) atomic_numbers_tensor = element_to_atomic_number(atomic_numbers_list) # Construct the final dictionary result_dict = { "pos": positions_tensor, "cell": torch.tensor(data["entries"][k]["structure"]["lattice"]["matrix"]), "atomic_numbers": atomic_numbers_tensor, "energy": data["entries"][k]["energy"], "force": torch.zeros( (len(d), 3) ), # Assuming a 3D force vector for each site "pbc": torch.tensor([1, 1, 1]), } # Convert to ASE and perform relaxation (assuming these functions are defined elsewhere) atoms = convert_to_ase(result_dict) # batch = convAtomstoBatch(atoms) OptimTraj, OptimEnergies = FIRE_Relax( atoms, calculator, NStep=args.FIRE_steps, ftol=1e-5, plot=False ) Pred_Energy = OptimEnergies[-1] formation_pred = ( Pred_Energy - sum_data["total energy"].iloc[struc_id] + sum_data["formation energy"].iloc[struc_id] ) d_hull_pred = formation_pred - sum_data["d_hull"].iloc[struc_id] struc_id += 1 Actual_Energy = result_dict["energy"] results["Predictions_e"].append(Pred_Energy) results["Actuals_e"].append(Actual_Energy) results["formation_pred"].append(formation_pred) results["d_hull_pred"].append(d_hull_pred) results["Pred_Energy"].append(Pred_Energy) # Update the sum_data DataFrame sum_data.at[struc_id - 1, "formation_pred"] = formation_pred sum_data.at[struc_id - 1, "Pred_Energy"] = Pred_Energy sum_data.at[struc_id - 1, "d_hull_pred"] = d_hull_pred return results def main(args): # with bz2.open(args.data_path) as fh: # data = json.loads(fh.read().decode("utf-8")) col = [ "composition", "n_sites", "volume", "total energy", "formation energy", "d_hull", "band_gap", "id", ] sum_data = pd.read_csv(args.data_summary, header=None) sum_data.columns = col # results = process_data_loader(data, sum_data) sum_data.to_csv(args.out_path, index=False) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run MD simulation with MACE model") parser.add_argument( "--model_path", type=str, default="example/lips20/botnet/swa_model.pth", help="Path to the model", ) parser.add_argument( "--device", type=str, default="cpu", help="Device:['cpu','cuda']" ) parser.add_argument( "--data_path", type=str, default="out_dir_sl/neqip/lips20/", help="input data path", ) parser.add_argument( "--data_summary", type=str, default="out_dir_sl/neqip/lips20/", help="data summary path", ) parser.add_argument( "--out_path", type=str, default="/home/civil/phd/cez218288/Benchmarking/MDBENCHGNN/mace_universal_2.0/WBM/updated_step_1.csv", help="Output path", ) parser.add_argument("--FIRE_steps", type=int, default=10, help="optimization steps") args = parser.parse_args() main(args)