import os import time from pathlib import Path import numpy as np import pandas as pd from ase import Atoms, units from ase.calculators.calculator import Calculator from ase.md import MDLogger from ase.md.nptberendsen import NPTBerendsen from ase.md.velocitydistribution import MaxwellBoltzmannDistribution from loguru import logger from tqdm import tqdm from utils import (get_density, minimize_structure, replicate_system, symmetricize_replicate,elastic_tensor_calculation) # Define a function to determine the new interval def get_new_interval(current_step): if current_step < 100: return 1 return 10 def run_simulation( calculator: Calculator, atoms: Atoms, pressure: float = 0.000101325, # GPa temperature: float = 298, timestep: float = 0.1, steps: int = 10, SimDir: str | Path = Path.cwd(), traj_dump_interval: int = 10, debug: bool = False, aim_experiment=None, ): # Define the temperature and pressure init_conf = atoms init_conf.set_calculator(calculator) # Initialize the NPT dynamics MaxwellBoltzmannDistribution(init_conf, temperature_K=temperature) starting_temperature = temperature dyn = NPTBerendsen( init_conf, timestep=timestep * units.fs, temperature_K=temperature, pressure_au=pressure * units.bar, compressibility_au=4.57e-5 / units.bar, ) # Initialize the logger with an initial interval initial_interval = get_new_interval(0) md_logger = MDLogger( dyn, init_conf, os.path.join(SimDir, "Simulation_thermo.log"), header=True, stress=True, peratom=False, mode="w", ) # Attach the logger with the initial interval dyn.attach(md_logger, interval=initial_interval) # Function to update the logger interval dynamically def update_logger_interval(): current_step = dyn.get_number_of_steps() new_interval = get_new_interval(current_step) md_logger.interval = new_interval update_interval = 10 # Adjust this value as needed dyn.attach(update_logger_interval, interval=update_interval) density = [] angles = [] lattice_parameters = [] def write_frame(): dyn.atoms.write( os.path.join(SimDir, f"MD_{atoms.get_chemical_formula()}_NPT.xyz"), append=True, ) cell = dyn.atoms.get_cell() lattice_parameters.append(cell.lengths()) # Get the lattice parameters angles.append(cell.angles()) # Get the angles density.append(get_density(atoms)) dyn.attach(write_frame, interval=traj_dump_interval) counter = 0 len_time_list = 0 len_temperature_list = 0 time_list = [] temperature_list = [] for k in tqdm(range(steps), desc="Running dynamics integration.", total=steps): dyn_time_start = time.time() dyn.run(1) dyn_step_time = time.time() - dyn_time_start if len_time_list > 9: time_list.pop(0) time_list.append(dyn_step_time) else: time_list.append(dyn_step_time) len_time_list = len(time_list) if len_temperature_list > 9: temperature_list.pop(0) temperature_list.append(dyn.atoms.get_temperature()) diffs = [b - a for a, b in zip(temperature_list, temperature_list[1:])] diff_check = [ diffs[idx] > 10 * temperature_list[idx - 1] for idx in range(1, len(diffs)) ] if all(temp > 3_000 for temp in temperature_list) or all(diff_check): aim_experiment.log({"temp_check_stopped": True}, k) break else: temperature_list.append(dyn.atoms.get_temperature()) len_temperature_list = len(temperature_list) counter += 1 if counter % 100 == 0: total_energy = atoms.get_total_energy() max_force = np.max(np.abs(atoms.get_forces())) if not debug: aim_experiment.log( { "step": k, "density": density[-1], "rolling_avg_step_time": sum(time_list) / 10, "temp_rolling_avg": sum(temperature_list) / 10, "total_energy": total_energy, "max_force": max_force, }, k, ) if k < 100: write_frame() density = np.array(density) angles = np.array(angles) lattice_parameters = np.array(lattice_parameters) # Calculate average values avg_density = np.mean(density) avg_angles = np.mean(angles, axis=0) avg_lattice_parameters = np.mean(lattice_parameters, axis=0) return avg_density, avg_angles, avg_lattice_parameters def run_relaxation(atoms, minimize_steps, debug: bool = False, aim_experiment=None): minimize_time_start = time.time() atoms = minimize_structure(atoms, steps=minimize_steps) relaxation_time = time.time() - minimize_time_start if not debug: aim_experiment.log({"relaxation_time": relaxation_time}) # Calculate density and cell lengths and angles density = get_density(atoms) cell_params = atoms.get_cell_lengths_and_angles().tolist() return atoms, density, cell_params def run_elastic_tensor(atoms, args, temperature, pressure, file, aim_experiment): data = [] # Replicate_system replication_factors, size = symmetricize_replicate( len(atoms), max_atoms=args.max_atoms, box_lengths=atoms.get_cell_lengths_and_angles()[:3], ) atoms = replicate_system(atoms, replication_factors) if not args.debug: aim_experiment.log({"num_atoms": atoms.positions.shape[0]}) # Minimize the structure atoms, density, cell_params = run_relaxation( atoms, args.minimize_steps, args.debug, aim_experiment ) sim_dir = os.path.join(args.results_dir, f"{args.index}_Simulation_{file}") logger.info(f"Simulation directory: {sim_dir}") elastic_file=os.path.join(sim_dir,f'elastic_plot_{file}.csv') # Uncomment this for elastic tensor calculation os.makedirs(sim_dir, exist_ok=True) simulation_time_start = time.time() elastic_tensor=elastic_tensor_calculation(atoms,atoms.calc, elastic_file) avg_density, avg_angles, avg_lattice_parameters = run_simulation( atoms.calc, atoms, pressure=pressure, temperature=temperature, timestep=args.timestep, steps=1, SimDir=sim_dir, traj_dump_interval=args.trajdump_interval, debug=args.debug, aim_experiment=aim_experiment, ) simulation_time = time.time() - simulation_time_start if not args.debug: aim_experiment.log({"simulation_time": simulation_time}) # Append the results to the data list data.append( [file[:-4], density] + cell_params + [avg_density] + avg_lattice_parameters.tolist() + avg_angles.tolist() + [elastic_tensor[i,j] for i in range(6) for j in range(6)] ) # Log final results to aim total_energy = atoms.get_total_energy() max_force = np.max(np.abs(atoms.get_forces())) if not args.debug: aim_experiment.log( { "exp_density": density, "avg_density": avg_density, "final_total_energy": total_energy, "final_max_force": max_force, } ) # Create a DataFrame columns = [ "Filename", "Exp_Density (g/cm³)", "Exp_a (Å)", "Exp_b (Å)", "Exp_c (Å)", "Exp_alpha (°)", "Exp_beta (°)", "Exp_gamma (°)", "Sim_Density (g/cm³)", "Sim_a (Å)", "Sim_b (Å)", "Sim_c (Å)", "Sim_alpha (°)", "Sim_beta (°)", "Sim_gamma (°)", ]+ [f"c{i+1}{j+1}" for i in range(6) for j in range(6)] df = pd.DataFrame(data, columns=columns) # Save the DataFrame to a CSV file df.to_csv(os.path.join(sim_dir, "Data.csv"), index=False) def run(atoms, args, temperature, pressure, file, aim_experiment): data = [] # Replicate_system replication_factors, size = symmetricize_replicate( len(atoms), max_atoms=args.max_atoms, box_lengths=atoms.get_cell_lengths_and_angles()[:3], ) atoms = replicate_system(atoms, replication_factors) if not args.debug: aim_experiment.log({"num_atoms": atoms.positions.shape[0]}) # Minimize the structure atoms, density, cell_params = run_relaxation( atoms, args.minimize_steps, args.debug, aim_experiment ) sim_dir = os.path.join(args.results_dir, f"{args.index}_Simulation_{file}") logger.info(f"Simulation directory: {sim_dir}") # elastic_file=os.path.join(sim_dir,f'elastic_plot_{file}.csv') # Uncomment this for elastic tensor calculation os.makedirs(sim_dir, exist_ok=True) simulation_time_start = time.time() # elastic_tensor=elastic_tensor_calculation(atoms,atoms.calc, elastic_file) avg_density, avg_angles, avg_lattice_parameters = run_simulation( atoms.calc, atoms, pressure=pressure, temperature=temperature, timestep=args.timestep, steps=args.runsteps, SimDir=sim_dir, traj_dump_interval=args.trajdump_interval, debug=args.debug, aim_experiment=aim_experiment, ) simulation_time = time.time() - simulation_time_start if not args.debug: aim_experiment.log({"simulation_time": simulation_time}) # Append the results to the data list data.append( [file[:-4], density] + cell_params + [avg_density] + avg_lattice_parameters.tolist() + avg_angles.tolist() #+ [elastic_tensor[i,j] for i in range(6) for j in range(6)] ) # Log final results to aim total_energy = atoms.get_total_energy() max_force = np.max(np.abs(atoms.get_forces())) if not args.debug: aim_experiment.log( { "exp_density": density, "avg_density": avg_density, "final_total_energy": total_energy, "final_max_force": max_force, } ) # Create a DataFrame columns = [ "Filename", "Exp_Density (g/cm³)", "Exp_a (Å)", "Exp_b (Å)", "Exp_c (Å)", "Exp_alpha (°)", "Exp_beta (°)", "Exp_gamma (°)", "Sim_Density (g/cm³)", "Sim_a (Å)", "Sim_b (Å)", "Sim_c (Å)", "Sim_alpha (°)", "Sim_beta (°)", "Sim_gamma (°)", ]#+ [f"c{i+1}{j+1}" for i in range(6) for j in range(6)] df = pd.DataFrame(data, columns=columns) # Save the DataFrame to a CSV file df.to_csv(os.path.join(sim_dir, "Data.csv"), index=False)