VASP机器学习力场（MLFF）是一种基于机器学习技术的力场，具有许多优点。MLFF可以显著加速分子动力学模拟。通过使用该机器学习模型来预测能量和力，以及体系的动力学演化过程，从而大大减少计算时间和资源消耗。以下是VASP进行机器学习力场计算的详细步骤。
#本文摘自github sona2576的例子
步骤一：用VASP进行分子动力学计算从头开始进行即时训练
准备所需的POSCAR起始构型和适当的INCAR、KPOINTS和POTCAR文件。设置ML_LMLFF和ML_MODE标签，启用MLFF功能并设置为训练模式。VASP将根据需要自动执行从头计算，并依靠MLFF的预测结果。输出文件包括OUTCAR、XDATCAR等，同时还会生成MLFF相关的文件（ML_LOGFILE，ML_ABN ，ML_FFN ）。INCAR如下：
SYSTEM = Li2SiS3_tetragonal_heating### Electronic structure partPREC = NormalADDGRID = .TRUE.GGA = PSALGO = FastENCUT = 650EDIFF = 1e-6NELM = 400NELMIN = 6ISPIN = 1ISYM = 0ISMEAR = 0SIGMA = 0.05### MD partIBRION = 0 #sets up MDISIF = 3 #allows cell shape and volume changeMDALGO = 3 #sets langevin thermostatSMASS = -1 #ensure T scaling every NBLOCK stepsNSW = 10000POTIM = 2NCORE = 20 #set for use in michael A nodes. Make it multiples of 8 or 16 for use in isambard or archerNBLOCK = 20TEBEG = 10TEEND = 300RANDOM_SEED = 743491 0 0 #useful to set a random seed for training runs and use it throughoutLANGEVIN_GAMMA = 10.0 10.0 10.0 #langevin thermostat parametersLANGEVIN_GAMMA_L = 3.0### Output partLREAL = AutoLWAVE = .FALSE.LCHARG = .FALSE.### ML primary tagsML_LMLFF = .TRUE. #ML training, use is onML_ISTART = 0 #training is starting from scratchML_CX = -0.1 #to increase frequency of Ab-initio steps, recommended for training at low TML_MB = 3000 #max # of local configurations stored for each element
这里做了温度从10K到300K Li2SSi3的升温分子动力学过程力场训练
以下是RMSE机器学习力场与AIMD对比结果，RMSE（均方差），能量，力，径向分布函数，VACF（速度自相关函数），声子态密度，MSD（均方位移）的对比处理python脚本。
import numpy as npimport plotly.graph_objects as goimport plotly.io as pio# plotting learning rate wrt steps! grep STATUS ML_LOGFILE|grep -E 'learning'|grep -v "#" > ./learning.dat! grep STATUS ML_LOGFILE|grep -E 'critical'|grep -v "#" > ./critical.dat! grep STATUS ML_LOGFILE|grep -E 'accurate'|grep -v "#" > ./accurate.datsteps_learning = np.loadtxt("./learning.dat",usecols=[1], unpack=True)y_learning = [3]*len(steps_learning)steps_critical = np.loadtxt("./critical.dat",usecols=[1], unpack=True)y_critical = [2]*len(steps_critical)steps_accurate = np.loadtxt("./accurate.dat",usecols=[1], unpack=True)y_accurate = [1]*len(steps_accurate)fig = go.Figure()fig.add_trace(go.Scatter(x = steps_learning, y = y_learning, mode='markers', name='learning'))fig.add_trace(go.Scatter(x = steps_critical, y = y_critical, mode='markers', name='critical'))fig.add_trace(go.Scatter(x = steps_accurate, y = y_accurate, mode='markers', name='accurate'))fig.update_yaxes(visible=False)fig.update_xaxes(title='steps')fig.update_layout(title="Monitoring quality of FF steps, 10 - 300 K run")fig.show()pio.write_image(fig, './10-300K_status.png', format='png')
! grep ERR ML_LOGFILE|grep -v "#"|awk '{print $2, $4}' > ERR.dat! grep BEEF ML_LOGFILE|grep -v "#"|awk '{print $2, $4}' > BEEF.dat! grep BEEF ML_LOGFILE|grep -v "#"|awk '{print $2, $6}' > CTIFOR.datsteps_err, err = np.loadtxt('./ERR.dat', unpack=True)steps_beef, beef = np.loadtxt('./BEEF.dat', unpack=True)steps_beef, ctifor = np.loadtxt('./CTIFOR.dat', unpack=True)fig2 = go.Figure()fig2.add_trace(go.Scatter(x=steps_err, y = err, mode='lines+markers', name='ERR'))fig2.add_trace(go.Scatter(x=steps_beef, y = beef, mode='lines', name='BEEF'))fig2.add_trace(go.Scatter(x=steps_beef, y = ctifor, mode='lines', name='CTIFOR'))fig2.update_layout(title='RMSE, Bayesian error, and CTIFOR (eV/A)')fig2.update_xaxes(title='steps')fig2.update_yaxes(title='error in force, eV/A')fig2.show()pio.write_image(fig2, './10-300K_err_beef_ctifor.png', format='png')
! grep LCONF ML_LOGFILE| grep -v "#" > LCONF.datsteps, liconf_old, liconf_new, siconf_old, siconf_new, sconf_old, sconf_new = np.loadtxt("./LCONF.dat",usecols=[1, 3, 4, 6, 7, 9, 10], unpack=True)fig1 = go.Figure()fig1.add_trace(go.Scatter(x = steps, y = liconf_old, mode='lines+markers', name='Li conf old', marker={'color' : 'darkblue', 'opacity' : 0.5}))fig1.add_trace(go.Scatter(x = steps, y = liconf_new, mode='lines+markers', name='Li conf new', marker={'color' : 'blue', 'opacity' : 0.5}))fig1.add_trace(go.Scatter(x = steps, y = siconf_new, mode='lines+markers', name='Si conf new', marker={'color' : 'red', 'opacity' : 0.5}))fig1.add_trace(go.Scatter(x = steps, y = siconf_old, mode='lines+markers', name='Si conf old', marker={'color' : 'darkred', 'opacity' : 0.5}))fig1.add_trace(go.Scatter(x = steps, y = sconf_new, mode='lines+markers', name='S conf new', marker={'color' : 'limegreen', 'opacity' : 0.5}))fig1.add_trace(go.Scatter(x = steps, y = sconf_old, mode='lines+markers', name='S conf old', marker={'color' : 'olive', 'opacity' : 0.5}))fig1.update_layout(title='Number of configurations added per element each learning step')fig1.update_xaxes(title='steps')fig1.update_yaxes(title='number of local configurations')fig1.show()pio.write_image(fig1, './300K_lconf.png', format='png')
from pymatgen.core import Structurefrom pymatgen.io.vasp.outputs import Xdatcar, Vasprunimport osfrom os import pathimport xml.etree.ElementTree as ETimport numpy as npfrom vasppy.outcar import forces_from_outcar, final_energy_from_outcarimport matplotlib.pyplot as pltimport scipy.stats as stats%matplotlib inline%config InlineBackend.figure_format='retina'import figure_formatting.figure_formatting as ffff.set_formatting() # set default formatting.dft_relaxed = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/14%P_cont_validation/14%P-doped-system/energy_force_testing/DFT/'mlff_relaxed_lite = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/14%P_cont_validation/14%P-doped-system/energy_force_testing/ML_FF_litercut5/'def plot_energy_diff(dft_dir1, mlff_dir1):dft_energies = []mlff_energies = []# go to dft directory and get the number of folders in this directory with integer namesos.chdir(dft_dir1)n_structures1 = len([name for name in os.listdir(".") if os.path.isdir(name)])for i in range(1, n_structures1+1):e = final_energy_from_outcar(dft_dir1 + f'{i}/' + 'OUTCAR')n_atoms = len(Structure.from_file(f'{dft_dir1}{i}/POSCAR'))dft_energies.append(float(e/n_atoms))m = final_energy_from_outcar(mlff_dir1 + f'{i}/' + 'OUTCAR')mlff_energies.append(float(m/n_atoms))# define root means square error, max error and mean errorrmse = np.sqrt(np.mean(np.square(np.array(dft_energies) - np.array(mlff_energies))))max_error = max(abs(np.array(dft_energies) - np.array(mlff_energies)))mean_avg_error = np.mean(abs(np.array(dft_energies) - np.array(mlff_energies)))plt.figure(figsize=(7, 3))plt.subplot(1, 2, 1)plt.scatter(dft_energies, mlff_energies, color = 'blue', s=10)plt.xlabel('DFT energies, eV/atom', fontsize=12)plt.ylabel('MLFF energies, eV/atom', fontsize=12)plt.text(0.05, 0.95, f'RMSE: {rmse:.3f} \n MAX: {max_error:.3f} \n MAE: {mean_avg_error:.3f}',transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')plt.minorticks_on()# plt.legend(loc='center', fontsize=10)plt.xticks(fontsize=10, rotation=45)plt.yticks(fontsize=10)# plt.colorbar()e_diff = np.array(dft_energies) - np.array(mlff_energies)plt.subplot(1, 2, 2)plt.scatter(range(len(e_diff)), e_diff*1000, color = 'grey', s=10)plt.xlabel('Structures', fontsize=12)plt.ylabel('(DFT-MLFF) Energy, meV/atom', fontsize=12)plt.minorticks_on()plt.xticks(fontsize=10)plt.yticks(fontsize=10)plt.tight_layout()plt.show()# Call the function with your directory pathsplot_energy_diff(dft_relaxed, mlff_relaxed_lite)
def plot_forces(species, dft_dir1, mlff_dir1):os.chdir(dft_dir1)n_structures1 = len([name for name in os.listdir(".") if os.path.isdir(name)])# determining indices of given speciesstructure = Structure.from_file(f'{dft_dir1}{1}/POSCAR')species_list = []for atom in structure:species_list.append(atom.species_string)if species not in species_list:raise ValueError("Selected species not present in structure")plot_indices = []for index, atom in enumerate(species_list):if atom == species:plot_indices.append(index)# dictionary of ml forcesdef get_forces(mlff_dir, dft_dir, n_structures):ml_run = {}for step in range(1, n_structures+1):tree = ET.parse(f'{mlff_dir}{step}/vasprun.xml')root = tree.getroot()for structure in root.findall('varray'):if 'forces' in structure.attrib['name']:ml_run[step] = {}for atom_index, l in enumerate(structure):l = str(l.text).strip()l = l.split(" ")forces_atom = [float(l[0]),float(l[1]),float(l[2])]ml_run[step][atom_index] = np.linalg.norm(forces_atom)dft_run = {}for step in range(1, n_structures+1):dft_run[step] = {}forces = forces_from_outcar(f'{dft_dir}{step}/OUTCAR')for atoms in range(0, len(forces[0])):dft_run[step][atoms] = np.linalg.norm(forces[0][atoms])return ml_run, dft_runml_run1, dft_run1 = get_forces(mlff_dir1, dft_dir1, n_structures1)def get_scalar_forces(ml_run, dft_run, n_structures):ml_force = []dft_force = []for step in range(1, n_structures+1):for atom in ml_run[step]:if atom in plot_indices:scalar_force = ml_run[step][atom]ml_force.append(scalar_force)for step in range(1, n_structures+1):for atom in dft_run[step]:if atom in plot_indices:scalar_force = dft_run[step][atom]dft_force.append(scalar_force)return ml_force, dft_forceml_force, dft_force = get_scalar_forces(ml_run1, dft_run1, n_structures1)return ml_force, dft_forceml_force_li, dft_force_li = plot_forces('Li', dft_relaxed, mlff_relaxed_lite)ml_force_si, dft_force_si = plot_forces('Si', dft_relaxed, mlff_relaxed_lite)ml_force_p, dft_force_p = plot_forces('P', dft_relaxed, mlff_relaxed_lite)ml_force_s, dft_force_s = plot_forces('S', dft_relaxed, mlff_relaxed_lite)total_force_ml = ml_force_li + ml_force_si + ml_force_p + ml_force_stotal_force_dft = dft_force_li + dft_force_si + dft_force_p + dft_force_srmse = np.sqrt(np.mean(np.square(np.array(total_force_dft) - np.array(total_force_ml))))max_error = max(abs(np.array(total_force_dft) - np.array(total_force_ml)))mean_avg_error = np.mean(abs(np.array(total_force_dft) - np.array(total_force_ml)))e_diff = np.array(total_force_dft) - np.array(total_force_ml)plt.figure(figsize=(7, 3))plt.subplot(1, 2, 1)plt.scatter(total_force_dft, total_force_ml, color = 'blue', s=10)plt.xlabel('DFT Forces, eV/A', fontsize=12)plt.ylabel('MLFF Forces, eV/A', fontsize=12)plt.text(0.05, 0.95, f'RMSE: {rmse:.3f} \n MAX: {max_error:.3f} \n MAE: {mean_avg_error:.3f}',transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')plt.minorticks_on()plt.xticks(fontsize=10)plt.yticks(fontsize=10)plt.subplot(1, 2, 2)plt.scatter(range(len(e_diff)), e_diff, color = 'grey', s=10)plt.xlabel(f'Atoms', fontsize=12)plt.ylabel('(DFT-MLFF) Forces, eV/A', fontsize=12)plt.minorticks_on()plt.xticks(fontsize=10)plt.yticks(fontsize=10)plt.tight_layout()plt.show()
from pymatgen.core import Structurefrom pymatgen.io.vasp.outputs import Xdatcarimport osfrom os import pathimport xml.etree.ElementTree as ETfrom pymatgen.io.vasp.outputs import Vasprunimport numpy as npfrom plotly.subplots import make_subplotsimport plotly.graph_objects as goimport plotly.io as pioAIMD_dir_780 = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/aimd/780_aimd/'ML_dir_780 = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/MD/780_md/'AIMD_dir_500 = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/aimd/500_aimd/'ML_dir_500 = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/MD/500_md/'AIMD_dir_300 = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/aimd/300_aimd/'ML_dir_300 = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/MD/300_md/'f_u = 4supercell = 3*3*2outcar_a_780 = f'{AIMD_dir_780}OUTCAR'outcar_a_500 = f'{AIMD_dir_500}OUTCAR'outcar_a_300 = f'{AIMD_dir_300}OUTCAR'oszicar_a_780 = f'{AIMD_dir_780}OSZICAR'oszicar_a_500 = f'{AIMD_dir_500}OSZICAR'oszicar_a_300 = f'{AIMD_dir_300}OSZICAR'outcar_m_780 = f'{ML_dir_780}OUTCAR'outcar_m_500 = f'{ML_dir_500}OUTCAR'outcar_m_300 = f'{ML_dir_300}OUTCAR'oszicar_m_780 = f'{ML_dir_780}OSZICAR'oszicar_m_500 = f'{ML_dir_500}OSZICAR'oszicar_m_300 = f'{ML_dir_300}OSZICAR'volume_a_780 = []volume_a_500 = []volume_a_300 = []volume_m_780 = []volume_m_500 = []volume_m_300 = []T_a_780 = []T_a_500 = []T_a_300 = []T_m_780 = []T_m_500 = []T_m_300 = []with open(outcar_a_780, mode = 'r') as file:lines = file.readlines()for line in lines:if "volume of cell :" in line:volume_a_780.append(float(line.split()[4])/f_u/supercell)with open(outcar_a_500, mode = 'r') as file:lines = file.readlines()for line in lines:if "volume of cell :" in line:volume_a_500.append(float(line.split()[4])/f_u/supercell)with open(outcar_a_300, mode = 'r') as file:lines = file.readlines()for line in lines:if "volume of cell :" in line:volume_a_300.append(float(line.split()[4])/f_u/supercell)with open(oszicar_a_780, mode = 'r') as file:lines = file.readlines()for line in lines:if "T=" in line:T_a_780.append(float(line.split()[2]))with open(oszicar_a_500, mode = 'r') as file:lines = file.readlines()for line in lines:if "T=" in line:T_a_500.append(float(line.split()[2]))with open(oszicar_a_300, mode = 'r') as file:lines = file.readlines()for line in lines:if "T=" in line:T_a_300.append(float(line.split()[2]))with open(outcar_m_780, mode = 'r') as file:lines = file.readlines()for line in lines:if "volume of cell :" in line:volume_m_780.append(float(line.split()[4])/f_u/supercell)with open(outcar_m_500, mode = 'r') as file:lines = file.readlines()for line in lines:if "volume of cell :" in line:volume_m_500.append(float(line.split()[4])/f_u/supercell)with open(outcar_m_300, mode = 'r') as file:lines = file.readlines()for line in lines:if "volume of cell :" in line:volume_m_300.append(float(line.split()[4])/f_u/supercell)with open(oszicar_m_780, mode = 'r') as file:lines = file.readlines()for line in lines:if "T=" in line:T_m_780.append(float(line.split()[2]))with open(oszicar_m_500, mode = 'r') as file:lines = file.readlines()for line in lines:if "T=" in line:T_m_500.append(float(line.split()[2]))with open(oszicar_m_300, mode = 'r') as file:lines = file.readlines()for line in lines:if "T=" in line:T_m_300.append(float(line.split()[2]))fig = make_subplots(rows=1, cols=1)fig.add_trace(go.Scatter(x=T_a_780, y=volume_a_780, mode='markers', name='AIMD 780', marker={'opacity' : 0.5}),row=1, col=1)fig.add_trace(go.Scatter(x=T_m_780, y=volume_m_780, mode='markers', name='MLFF 780', marker={'opacity' : 0.5}),row=1, col=1)fig.add_trace(go.Scatter(x=T_a_500, y=volume_a_500, mode='markers', name='AIMD 500', marker={'opacity' : 0.5}),row=1, col=1)fig.add_trace(go.Scatter(x=T_m_500, y=volume_m_500, mode='markers', name='MLFF 500', marker={'opacity' : 0.5}),row=1, col=1)fig.add_trace(go.Scatter(x=T_a_300, y=volume_a_300, mode='markers', name='AIMD 300', marker={'opacity' : 0.5}),row=1, col=1)fig.add_trace(go.Scatter(x=T_m_300, y=volume_m_300, mode='markers', name='MLFF 300', marker={'opacity' : 0.5}),row=1, col=1)fig.update_xaxes(title_text='T, K')fig.update_yaxes(title_text='Volume/f.u,, A3')fig.show()
from pymatgen.io.vasp.outputs import Xdatcarfrom vasppy.rdf import RadialDistributionFunctionimport numpy as npimport matplotlib.pyplot as pltaimd_dir = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/aimd/14P-300/xdatcars/'mlff_dir_lite = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/14%P_from_scratch_validation/MSD-RDF-md/30ps-lite/'xd_aimd = Xdatcar(aimd_dir+'XDATCAR_collated_1-3')rdf_lili = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='Li')rdf_lisi = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='Li', species_j='Si')rdf_lis = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='Li', species_j='S')rdf_sisi = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='Si')rdf_sis = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='Si', species_j='S')rdf_ss = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='S')rdf_lip = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='Li', species_j='P')rdf_pp = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='P')rdf_sip = RadialDistributionFunction.from_species_strings(structures=xd_aimd.structures, species_i='Si', species_j='P')xd_ml = Xdatcar(mlff_dir_lite+'XDATCAR')rdf_lili_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='Li')rdf_lisi_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='Li', species_j='Si')rdf_lis_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='Li', species_j='S')rdf_sisi_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='Si')rdf_sis_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='Si', species_j='S')rdf_ss_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='S')rdf_lip_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='Li', species_j='P')rdf_pp_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='P')rdf_sip_m = RadialDistributionFunction.from_species_strings(structures=xd_ml.structures, species_i='Si', species_j='P')# plot the RDFsfig, axes = plt.subplots(3, 3, figsize=(6, 9), sharex=False, sharey=False)axes[0, 0].plot(rdf_lili.r, rdf_lili.rdf, color='red', label='AIMD')axes[0, 1].plot(rdf_sisi.r, rdf_sisi.rdf, color='red', label='AIMD')axes[0, 2].plot(rdf_ss.r, rdf_ss.rdf, color='red', label='AIMD')axes[1, 0].plot(rdf_lisi.r, rdf_lisi.rdf, color='red', label='AIMD')axes[1, 1].plot(rdf_lis.r, rdf_lis.rdf, color='red', label='AIMD')axes[1, 2].plot(rdf_sis.r, rdf_sis.rdf, color='red', label='AIMD')axes[2, 0].plot(rdf_lip.r, rdf_lip.rdf, color='red', label='AIMD')axes[2, 1].plot(rdf_pp.r, rdf_pp.rdf, color='red', label='AIMD')axes[2, 2].plot(rdf_sip.r, rdf_sip.rdf, color='red', label='AIMD')axes[0, 0].plot(rdf_lili_m.r, rdf_lili_m.rdf, color='green', label='3% P, 300K')axes[0, 1].plot(rdf_sisi_m.r, rdf_sisi_m.rdf, color='green', label='3% P, 300K')axes[0, 2].plot(rdf_ss_m.r, rdf_ss_m.rdf, color='green', label='3% P, 300K')axes[1, 0].plot(rdf_lisi_m.r, rdf_lisi_m.rdf, color='green', label='3% P, 300K')axes[1, 1].plot(rdf_lis_m.r, rdf_lis_m.rdf, color='green', label='3% P, 300K')axes[1, 2].plot(rdf_sis_m.r, rdf_sis_m.rdf, color='green', label='3% P, 300K')axes[2, 0].plot(rdf_lip_m.r, rdf_lip_m.rdf, color='green', label='3% P, 300K')axes[2, 1].plot(rdf_pp_m.r, rdf_pp_m.rdf, color='green', label='3% P, 300K')axes[2, 2].plot(rdf_sip_m.r, rdf_sip_m.rdf, color='green', label='3% P, 300K')# Add titles and labelsaxes[0, 0].set_title('Li-Li RDF', fontsize=10, loc='center')axes[0, 1].set_title('Si-Si RDF', fontsize=10, loc='center')axes[0, 2].set_title('S-S RDF', fontsize=10, loc='center')axes[1, 0].set_title('Li-Si RDF', fontsize=10, loc='center')axes[1, 1].set_title('Si-S RDF', fontsize=10, loc='center')axes[1, 2].set_title('Li-S RDF', fontsize=10, loc='center')axes[2, 0].set_title('Li-P RDF', fontsize=10, loc='center')axes[2, 1].set_title('P-P RDF', fontsize=10, loc='center')axes[2, 2].set_title('Si-P RDF', fontsize=10, loc='center')plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))axes[0, 0].set_ylabel('g(r)', fontsize=10)axes[1, 0].set_ylabel('g(r)', fontsize=10)axes[2, 0].set_ylabel('g(r)', fontsize=10)axes[1, 0].set_xlabel('r, A', fontsize=10)axes[1, 1].set_xlabel('r, A', fontsize=10)axes[1, 2].set_xlabel('r, A', fontsize=10)axes[0, 0].set_xlabel('r, A', fontsize=10)axes[0, 1].set_xlabel('r, A', fontsize=10)axes[0, 2].set_xlabel('r, A', fontsize=10)axes[2, 0].set_xlabel('r, A', fontsize=10)axes[2, 1].set_xlabel('r, A', fontsize=10)axes[2, 2].set_xlabel('r, A', fontsize=10)plt.tight_layout()plt.show()
import matplotlib.pyplot as pltimport numpy as npfrom py4vasp import Calculationimport scipy.linalgfrom tqdm import tqdmimport matplotlib.pyplot as plt%matplotlib inline%config InlineBackend.figure_format='retina'import figure_formatting.figure_formatting as ffff.set_formatting() # set default formatting.aimd_dir = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/aimd/14P-600/14%P-600-nvt-2/'mlff_dir_lite = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/14%P_cont_validation/14%P-doped-system/VACF-md/10ps_litercut5/'velocities_aimd = Calculation.from_path(aimd_dir).velocity[:690:1].read()['velocities']velocities_mlff_lite = Calculation.from_path(mlff_dir_lite).velocity[:690:1].read()['velocities']velocities_aimd_li = velocities_aimd[:, 0:134, :]velocities_mlff_li_lite = velocities_mlff_lite[:, 0:134, :]velocities_aimd_si = velocities_aimd[:, 134:196, :]velocities_mlff_si_lite = velocities_mlff_lite[:, 134:196, :]velocities_aimd_p = velocities_aimd[:, 196:206, :]velocities_mlff_p_lite = velocities_mlff_lite[:, 196:206, :]velocities_aimd_s = velocities_aimd[:, 206:422, :]velocities_mlff_s_lite = velocities_mlff_lite[:, 206:422, :]N_frames = velocities_aimd_li.shape[0] # use same number of frames for both calculationsN_block = 5timestep = 2 # in fsdt = timestep * N_block # single interval length for velocity outputingt = np.arange(0, N_frames*dt, dt) # time arraydef vacf(velocities, atoms):correlation_matrix = []for n in tqdm(range(atoms)):correlation_list = []for k in range(0, len(velocities), 1):buffer_sum = 0denominator = len(range(0, len(velocities) - k))for j in range(0, len(velocities) - k):velocity_vector_1 = velocities[j][n, :]velocity_vector_2 = velocities[k + j][n, :]dot_product = np.sum(velocity_vector_1 * velocity_vector_2)norm_product = scipy.linalg.norm(velocity_vector_1) * scipy.linalg.norm(velocity_vector_2)buffer_sum += dot_product / norm_product# buffer sum is the sum of the correlation terms for each moving framecorrelation_list.append(buffer_sum / denominator)# correlation list is the averaged correlation values for each framecorrelation_matrix.append(correlation_list)# in correlation matrix we end up withcorrelation_matrix = np.array(correlation_matrix)correlation_matrix = np.mean(correlation_matrix, axis=0)return correlation_matrixvacf_aimd_p = vacf(velocities_aimd_p, 10)vacf_mlff_p_lite = vacf(velocities_mlff_p_lite, 10)plt.figure(figsize=(3, 3))plt.plot(t/1000, vacf_aimd_p, 'b-', linewidth=1, label='AIMD')plt.plot(t/1000, vacf_mlff_p_lite, 'r-', linewidth=1, label='MLFF')plt.legend(loc='best')plt.xlabel('Time (ps)', fontsize=12)plt.ylabel('VACF', fontsize=12)plt.xlim(0, 1.5)plt.xticks(fontsize=10)plt.yticks(fontsize=10)plt.tight_layout()plt.show()
import matplotlib.pyplot as pltimport numpy as npfrom py4vasp import Calculationimport scipy.linalgfrom tqdm import tqdmimport matplotlib.pyplot as plt%matplotlib inline%config InlineBackend.figure_format='retina'import figure_formatting.figure_formatting as ffff.set_formatting() # set default formatting.aimd_dir = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/aimd/14P-600/14%P-600-nvt-2/'mlff_dir_lite = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/doped/14%P_cont_validation/14%P-doped-system/VACF-md/10ps_litercut5/'velocities_aimd = Calculation.from_path(aimd_dir).velocity[:690:1].read()['velocities']velocities_mlff_lite = Calculation.from_path(mlff_dir_lite).velocity[:690:1].read()['velocities']velocities_aimd_li = velocities_aimd[:, 0:134, :]velocities_mlff_li_lite = velocities_mlff_lite[:, 0:134, :]velocities_aimd_si = velocities_aimd[:, 134:196, :]velocities_mlff_si_lite = velocities_mlff_lite[:, 134:196, :]velocities_aimd_p = velocities_aimd[:, 196:206, :]velocities_mlff_p_lite = velocities_mlff_lite[:, 196:206, :]velocities_aimd_s = velocities_aimd[:, 206:422, :]velocities_mlff_s_lite = velocities_mlff_lite[:, 206:422, :]N_frames = velocities_aimd_li.shape[0] # use same number of frames for both calculationsN_block = 5timestep = 2 # in fsdt = timestep * N_block # single interval length for velocity outputingt = np.arange(0, N_frames*dt, dt) # time arraydef vacf(velocities, atoms):correlation_matrix = []for n in tqdm(range(atoms)):correlation_list = []for k in range(0, len(velocities), 1):buffer_sum = 0denominator = len(range(0, len(velocities) - k))for j in range(0, len(velocities) - k):velocity_vector_1 = velocities[j][n, :]velocity_vector_2 = velocities[k + j][n, :]dot_product = np.sum(velocity_vector_1 * velocity_vector_2)norm_product = scipy.linalg.norm(velocity_vector_1) * scipy.linalg.norm(velocity_vector_2)buffer_sum += dot_product / norm_product# buffer sum is the sum of the correlation terms for each moving framecorrelation_list.append(buffer_sum / denominator)# correlation list is the averaged correlation values for each framecorrelation_matrix.append(correlation_list)# in correlation matrix we end up withcorrelation_matrix = np.array(correlation_matrix)correlation_matrix = np.mean(correlation_matrix, axis=0)return correlation_matrixvacf_aimd_p = vacf(velocities_aimd_p, 10)vacf_mlff_p_lite = vacf(velocities_mlff_p_lite, 10)plt.figure(figsize=(3, 3))plt.plot(t/1000, vacf_aimd_p, 'b-', linewidth=1, label='AIMD')plt.plot(t/1000, vacf_mlff_p_lite, 'r-', linewidth=1, label='MLFF')plt.legend(loc='best')plt.xlabel('Time (ps)', fontsize=12)plt.ylabel('VACF', fontsize=12)plt.xlim(0, 1.5)plt.xticks(fontsize=10)plt.yticks(fontsize=10)plt.tight_layout()plt.show()100%|██████████| 10/10 [00:42<00:00, 4.22s/it]100%|██████████| 10/10 [00:42<00:00, 4.23s/it]Taking Fourier transform of VACF gives us power spectrum. Good agreement in overall shape of power spectrum for every constituent element indicates that lattice vibrations for framework atoms (P, Si, S) and short time scale vibrational dynamics for diffusion ion (Li) is captured accurately by the forcefield.def VACF_fft(VACF, dt):VACF_fft = np.fft.fft(VACF)VACF_fft = np.fft.fftshift(VACF_fft)freq = np.fft.fftfreq(len(VACF), d=dt)freq = np.fft.fftshift(freq)return VACF_fft, freqVACF_fft_aimd, freq = VACF_fft(vacf_aimd_p, dt)VACF_fft_mlff_lite, freq = VACF_fft(vacf_mlff_p_lite, dt)plt.figure(figsize=(4, 3))plt.plot(freq*1000, VACF_fft_aimd.real, 'b-', linewidth=1, label='AIMD')plt.plot(freq*1000, VACF_fft_mlff_lite.real, 'r-', linewidth=1, label='MLFF')plt.xlabel('Frequency (THz)', fontsize=12)plt.ylabel('Phonon DOS', fontsize=12)plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))plt.xlim(0, 20)plt.minorticks_on()plt.xticks(fontsize=10)plt.yticks(fontsize=10)plt.tight_layout()plt.show()
from pymatgen.core import Structurefrom pymatgen.io.vasp.outputs import Xdatcarimport matplotlib.pyplot as pltimport numpy as npfrom kinisi.analyze import DiffusionAnalyzer, ConductivityAnalyzer, JumpDiffusionAnalyzerfrom kinisi.diffusion import Bootstrapfrom kinisi.arrhenius import StandardArrheniusimport warningswarnings.filterwarnings("ignore", category=UserWarning)np.random.seed(42)%matplotlib inline%config InlineBackend.figure_format='retina'import figure_formatting.figure_formatting as ffff.set_formatting() # set default formatting.aimd_dir = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/aimd/600/xdatcars/'mlff_dir_lite = '/Users/sona/Desktop/Research/project_2/tetragonal-li2sis3/MD/validationmd/xdatcars_600_mich/'temp = 600# for Aimdp_param_li = {'specie': 'Li','time_step': 20.0,'step_skip': 1}p_param_si = {'specie': 'Si','time_step': 20.0,'step_skip': 1}p_param_s = {'specie': 'S','time_step': 20.0,'step_skip': 1}# for MLp_param_li_m = {'specie': 'Li','time_step': 2.0,'step_skip': 10}p_param_si_m = {'specie': 'Si','time_step': 2.0,'step_skip': 10}p_param_s_m = {'specie': 'S','time_step': 2.0,'step_skip': 10}xd_a = Xdatcar(aimd_dir +'XDATCAR_collated_5-8')diff_li_a = DiffusionAnalyzer.from_Xdatcar(xd_a, parser_params=p_param_li)diff_si_a = DiffusionAnalyzer.from_Xdatcar(xd_a, parser_params=p_param_si)diff_s_a = DiffusionAnalyzer.from_Xdatcar(xd_a, parser_params=p_param_s)xd_m_lite = Xdatcar(mlff_dir_lite +'XDATCAR_collated_1-2')diff_li_m = DiffusionAnalyzer.from_Xdatcar(xd_m_lite, parser_params=p_param_li_m)diff_si_m = DiffusionAnalyzer.from_Xdatcar(xd_m_lite, parser_params=p_param_si_m)diff_s_m = DiffusionAnalyzer.from_Xdatcar(xd_m_lite, parser_params=p_param_s_m)plt.errorbar(diff_li_a.dt, diff_li_a.msd, diff_li_a.msd_std, label='Li MSD (AIMD)')plt.errorbar(diff_si_a.dt, diff_si_a.msd, diff_si_a.msd_std)plt.errorbar(diff_s_a.dt, diff_s_a.msd, diff_s_a.msd_std)plt.errorbar(diff_li_m.dt, diff_li_m.msd, diff_li_m.msd_std, label='Li MSD (MLFF)')plt.errorbar(diff_si_m.dt, diff_si_m.msd, diff_si_m.msd_std)plt.errorbar(diff_s_m.dt, diff_s_m.msd, diff_s_m.msd_std)plt.axvline(5, color='b')plt.ylabel('MSD/A$^2
plt.xlabel('$\Delta t$/ps')plt.xlim(0, 30)plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))plt.show()最后如果训练的力场不够精确，或者我们需要得到某一温度下更精确的力场可以，使用之前训练的模型进行进一步训练，注意需要将ML_ABN文件拷贝为ML_AB作为输入文件。INCAR具体如下：
SYSTEM = Li2SiS3_tetragonal_780_retrain### Electronic structure partPREC = NormalADDGRID = .TRUE.GGA = PSALGO = FastENCUT = 650EDIFF = 1e-6NELM = 400NELMIN = 6ISPIN = 1ISYM = 0ISMEAR = 0SIGMA = 0.05### MD partIBRION = 0ISIF = 3MDALGO = 3NSW = 0POTIM = 1NCORE = 32NBLOCK = 20TEBEG = 780TEEND = 780RANDOM_SEED = 743491 0 0LANGEVIN_GAMMA = 10.0 10.0 10.0LANGEVIN_GAMMA_L = 3.0### Output partLREAL = AutoLWAVE = .FALSE.LCHARG = .FALSE.### ML primary tagsML_LMLFF = .TRUE.ML_ISTART = 1ML_IALGO_LINREG = 3ML_RCUT2 = 6.0ML_RCUT1 = 6.0ML_CTIFOR = 1000ML_MB = 4000ML_LBASIS_DISCARD = .TRUE.
参考：
https://github.com/Sona2576/mlff_guide_scripts/blob/main/MLFF_guide.ipynbhttps://www.vasp.at/wiki/index.php/Machine_learning_force_field_calculations:_Basics