def make_deepmd_lammps(jdata, conf_dir, supercell): fp_params = jdata['vasp_params'] kspacing = fp_params['kspacing'] deepmd_model_dir = jdata['deepmd_model_dir'] deepmd_type_map = jdata['deepmd_type_map'] ntypes = len(deepmd_type_map) deepmd_model_dir = os.path.abspath(deepmd_model_dir) deepmd_models = glob.glob(os.path.join(deepmd_model_dir, '*pb')) deepmd_models_name = [os.path.basename(ii) for ii in deepmd_models] conf_path = os.path.abspath(conf_dir) conf_poscar = os.path.join(conf_path, 'POSCAR') # get equi poscar equi_path = re.sub('confs', global_equi_name, conf_path) equi_path = os.path.join(equi_path, 'vasp-k%.2f' % kspacing) equi_contcar = os.path.join(equi_path, 'CONTCAR') # equi_path = re.sub('confs', global_equi_name, conf_path) # equi_path = os.path.join(equi_path, 'lmp') # equi_dump = os.path.join(equi_path, 'dump.relax') task_path = re.sub('confs', global_task_name, conf_path) task_path = os.path.join(task_path, 'deepmd') os.makedirs(task_path, exist_ok=True) # gen task poscar task_poscar = os.path.join(task_path, 'POSCAR') # lammps.poscar_from_last_dump(equi_dump, task_poscar, deepmd_type_map) cwd = os.getcwd() os.chdir(task_path) if os.path.isfile('POSCAR'): os.remove('POSCAR') os.symlink(os.path.relpath(equi_contcar), 'POSCAR') os.chdir(cwd) # gen structure from equi poscar ss = Structure.from_file(task_poscar) # gen defects vds = VacancyGenerator(ss) dss = [] for jj in vds: dss.append(jj.generate_defect_structure(supercell)) # gen tasks cwd = os.getcwd() # make lammps.in, relax at 0 bar (scale = 1) fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, 1, lammps.inter_deepmd, deepmd_models_name) f_lammps_in = os.path.join(task_path, 'lammps.in') with open(f_lammps_in, 'w') as fp: fp.write(fc) # gen tasks copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2]) cwd = os.getcwd() for ii in range(len(dss)): struct_path = os.path.join(task_path, 'struct-%s-%03d' % (copy_str, ii)) print('# generate %s' % (struct_path)) os.makedirs(struct_path, exist_ok=True) os.chdir(struct_path) for jj in ['conf.lmp', 'lammps.in'] + deepmd_models_name: if os.path.isfile(jj): os.remove(jj) # make conf dss[ii].to('POSCAR', 'POSCAR') lammps.cvt_lammps_conf('POSCAR', 'conf.lmp') ptypes = vasp.get_poscar_types('POSCAR') lammps.apply_type_map('conf.lmp', deepmd_type_map, ptypes) # link lammps.in os.symlink(os.path.relpath(f_lammps_in), 'lammps.in') # link models for (ii, jj) in zip(deepmd_models, deepmd_models_name): os.symlink(os.path.relpath(ii), jj) # save supercell np.savetxt('supercell.out', supercell, fmt='%d') os.chdir(cwd)
def make_deepmd_lammps(jdata, conf_dir, max_miller = 2, static = False, relax_box = False, task_name = 'wrong-task') : fp_params = jdata['vasp_params'] kspacing = fp_params['kspacing'] deepmd_model_dir = jdata['deepmd_model_dir'] deepmd_type_map = jdata['deepmd_type_map'] ntypes = len(deepmd_type_map) deepmd_model_dir = os.path.abspath(deepmd_model_dir) deepmd_models = glob.glob(os.path.join(deepmd_model_dir, '*pb')) deepmd_models_name = [os.path.basename(ii) for ii in deepmd_models] min_slab_size = jdata['min_slab_size'] min_vacuum_size = jdata['min_vacuum_size'] # get equi poscar # conf_path = os.path.abspath(conf_dir) # conf_poscar = os.path.join(conf_path, 'POSCAR') equi_path = re.sub('confs', global_equi_name, conf_dir) equi_path = os.path.join(equi_path, 'vasp-k%.2f' % kspacing) equi_path = os.path.abspath(equi_path) equi_contcar = os.path.join(equi_path, 'CONTCAR') task_path = re.sub('confs', global_task_name, conf_dir) task_path = os.path.abspath(task_path) task_path = os.path.join(task_path, task_name) os.makedirs(task_path, exist_ok=True) cwd = os.getcwd() os.chdir(task_path) if os.path.isfile('POSCAR') : os.remove('POSCAR') os.symlink(os.path.relpath(equi_contcar), 'POSCAR') os.chdir(cwd) task_poscar = os.path.join(task_path, 'POSCAR') # gen strcture ss = Structure.from_file(task_poscar) # gen slabs all_slabs = generate_all_slabs(ss, max_miller, min_slab_size, min_vacuum_size) # make lammps.in if static : fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_deepmd, deepmd_models_name) else : fc = lammps.make_lammps_equi('conf.lmp', ntypes, lammps.inter_deepmd, deepmd_models_name, change_box = relax_box) f_lammps_in = os.path.join(task_path, 'lammps.in') with open(f_lammps_in, 'w') as fp : fp.write(fc) cwd = os.getcwd() for ii in range(len(all_slabs)) : slab = all_slabs[ii] miller_str = "m%d.%d.%dm" % (slab.miller_index[0], slab.miller_index[1], slab.miller_index[2]) # make dir struct_path = os.path.join(task_path, 'struct-%03d-%s' % (ii, miller_str)) os.makedirs(struct_path, exist_ok=True) os.chdir(struct_path) for jj in ['conf.lmp', 'lammps.in'] + deepmd_models_name : if os.path.isfile(jj): os.remove(jj) print("# %03d generate " % ii, struct_path, " \t %d atoms" % len(slab.sites)) # make conf slab.to('POSCAR', 'POSCAR') vasp.regulate_poscar('POSCAR', 'POSCAR') lammps.cvt_lammps_conf('POSCAR', 'conf.lmp') ptypes = vasp.get_poscar_types('POSCAR') lammps.apply_type_map('conf.lmp', deepmd_type_map, ptypes) # record miller np.savetxt('miller.out', slab.miller_index, fmt='%d') # link lammps.in os.symlink(os.path.relpath(f_lammps_in), 'lammps.in') # link models for (ii,jj) in zip(deepmd_models, deepmd_models_name) : os.symlink(os.path.relpath(ii), jj) cwd = os.getcwd()
def make_deepmd_lammps(jdata, conf_dir) : deepmd_model_dir = jdata['deepmd_model_dir'] deepmd_type_map = jdata['deepmd_type_map'] ntypes = len(deepmd_type_map) deepmd_model_dir = os.path.abspath(deepmd_model_dir) deepmd_models = glob.glob(os.path.join(deepmd_model_dir, '*pb')) deepmd_models_name = [os.path.basename(ii) for ii in deepmd_models] norm_def = jdata['norm_deform'] shear_def = jdata['shear_deform'] conf_path = os.path.abspath(conf_dir) conf_poscar = os.path.join(conf_path, 'POSCAR') # get equi poscar equi_path = re.sub('confs', global_equi_name, conf_path) equi_path = os.path.join(equi_path, 'deepmd') equi_dump = os.path.join(equi_path, 'dump.relax') task_path = re.sub('confs', global_task_name, conf_path) task_path = os.path.join(task_path, 'deepmd') os.makedirs(task_path, exist_ok=True) task_poscar = os.path.join(task_path, 'POSCAR') lammps.poscar_from_last_dump(equi_dump, task_poscar, deepmd_type_map) # get equi stress equi_log = os.path.join(equi_path, 'log.lammps') stress = lammps.get_stress(equi_log) np.savetxt(os.path.join(task_path, 'equi.stress.out'), stress) # gen strcture # ss = Structure.from_file(conf_poscar) # print(ss) # ss = ss.from_file(task_poscar) # print(ss) ss = Structure.from_file(task_poscar) # gen defomations norm_strains = [-norm_def, -0.5*norm_def, 0.5*norm_def, norm_def] shear_strains = [-shear_def, -0.5*shear_def, 0.5*shear_def, shear_def] print('gen with norm '+str(norm_strains)) print('gen with shear '+str(shear_strains)) dfm_ss = DeformedStructureSet(ss, symmetry = False, norm_strains = norm_strains, shear_strains = shear_strains) n_dfm = len(dfm_ss) # gen tasks cwd = os.getcwd() # make lammps.in fc = lammps.make_lammps_elastic('conf.lmp', ntypes, lammps.inter_deepmd, deepmd_models_name) f_lammps_in = os.path.join(task_path, 'lammps.in') with open(f_lammps_in, 'w') as fp : fp.write(fc) cwd = os.getcwd() for ii in range(n_dfm) : # make dir dfm_path = os.path.join(task_path, 'dfm-%03d' % ii) os.makedirs(dfm_path, exist_ok=True) os.chdir(dfm_path) for jj in ['conf.lmp', 'lammps.in'] + deepmd_models_name : if os.path.isfile(jj): os.remove(jj) # make conf dfm_ss.deformed_structures[ii].to('POSCAR', 'POSCAR') lammps.cvt_lammps_conf('POSCAR', 'conf.lmp') ptypes = vasp.get_poscar_types('POSCAR') lammps.apply_type_map('conf.lmp', deepmd_type_map, ptypes) # record strain strain = Strain.from_deformation(dfm_ss.deformations[ii]) np.savetxt('strain.out', strain) # link lammps.in os.symlink(os.path.relpath(f_lammps_in), 'lammps.in') # link models for (ii,jj) in zip(deepmd_models, deepmd_models_name) : os.symlink(os.path.relpath(ii), jj) cwd = os.getcwd()
def _make_meam_lammps(jdata, conf_dir, supercell, insert_ele, task_name): meam_potfile_dir = jdata['meam_potfile_dir'] meam_potfile_dir = os.path.abspath(meam_potfile_dir) meam_potfile = jdata['meam_potfile'] meam_potfile = [os.path.join(meam_potfile_dir, ii) for ii in meam_potfile] meam_potfile_name = jdata['meam_potfile'] type_map = jdata['meam_type_map'] ntypes = len(type_map) meam_param = { 'meam_potfile': jdata['meam_potfile'], 'meam_type': jdata['meam_param_type'] } conf_path = os.path.abspath(conf_dir) conf_poscar = os.path.join(conf_path, 'POSCAR') # get equi poscar equi_path = re.sub('confs', global_equi_name, conf_path) equi_path = os.path.join(equi_path, task_name) equi_dump = os.path.join(equi_path, 'dump.relax') task_path = re.sub('confs', global_task_name, conf_path) task_path = os.path.join(task_path, task_name) os.makedirs(task_path, exist_ok=True) task_poscar = os.path.join(task_path, 'POSCAR') cwd = os.getcwd() os.chdir(task_path) lammps.poscar_from_last_dump(equi_dump, task_poscar, type_map) os.chdir(cwd) # gen structure from equi poscar ss = Structure.from_file(task_poscar) # gen defects vds = InterstitialGenerator(ss, insert_ele) dss = [] for jj in vds: dss.append(jj.generate_defect_structure(supercell)) # gen tasks cwd = os.getcwd() # make lammps.in, relax at 0 bar (scale = 1) fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, 1, lammps.inter_meam, meam_param) f_lammps_in = os.path.join(task_path, 'lammps.in') with open(f_lammps_in, 'w') as fp: fp.write(fc) # gen tasks copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2]) cwd = os.getcwd() for ii in range(len(dss)): struct_path = os.path.join( task_path, 'struct-%s-%s-%03d' % (insert_ele, copy_str, ii)) print('# generate %s' % (struct_path)) os.makedirs(struct_path, exist_ok=True) os.chdir(struct_path) for jj in ['conf.lmp', 'lammps.in'] + meam_potfile_name: if os.path.isfile(jj): os.remove(jj) # make conf dss[ii].to('POSCAR', 'POSCAR') lammps.cvt_lammps_conf('POSCAR', 'conf.lmp') ptypes = vasp.get_poscar_types('POSCAR') lammps.apply_type_map('conf.lmp', type_map, ptypes) # link lammps.in os.symlink(os.path.relpath(f_lammps_in), 'lammps.in') # link models for (ii, jj) in zip(meam_potfile, meam_potfile_name): os.symlink(os.path.relpath(ii), jj) # save supercell np.savetxt('supercell.out', supercell, fmt='%d') os.chdir(cwd)
def _make_meam_reprod_traj(jdata, conf_dir, supercell, insert_ele, task_name): fp_params = jdata['vasp_params'] kspacing = fp_params['kspacing'] meam_potfile_dir = jdata['meam_potfile_dir'] meam_potfile_dir = os.path.abspath(meam_potfile_dir) meam_potfile = jdata['meam_potfile'] meam_potfile = [os.path.join(meam_potfile_dir, ii) for ii in meam_potfile] meam_potfile_name = jdata['meam_potfile'] type_map = jdata['meam_type_map'] ntypes = len(type_map) meam_param = { 'meam_potfile': jdata['meam_potfile'], 'meam_type': jdata['meam_param_type'] } conf_path = os.path.abspath(conf_dir) task_path = re.sub('confs', global_task_name, conf_path) vasp_path = os.path.join(task_path, 'vasp-k%.2f' % kspacing) lmps_path = os.path.join(task_path, task_name + '-k%.2f' % kspacing) os.makedirs(lmps_path, exist_ok=True) copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2]) struct_widecard = os.path.join(vasp_path, 'struct-%s-%s-*' % (insert_ele, copy_str)) vasp_struct = glob.glob(struct_widecard) vasp_struct.sort() cwd = os.getcwd() # make lammps.in fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_meam, meam_param) f_lammps_in = os.path.join(lmps_path, 'lammps.in') with open(f_lammps_in, 'w') as fp: fp.write(fc) for vs in vasp_struct: # get vasp energy outcar = os.path.join(vs, 'OUTCAR') energies = vasp.get_energies(outcar) # get xdat xdatcar = os.path.join(vs, 'XDATCAR') struct_basename = os.path.basename(vs) ls = os.path.join(lmps_path, struct_basename) print(ls) os.makedirs(ls, exist_ok=True) os.chdir(ls) if os.path.exists('XDATCAR'): os.remove('XDATCAR') os.symlink(os.path.relpath(xdatcar), 'XDATCAR') xdat_lines = open('XDATCAR', 'r').read().split('\n') natoms = vasp.poscar_natoms('XDATCAR') xdat_secsize = natoms + 8 xdat_nframes = len(xdat_lines) // xdat_secsize if xdat_nframes > len(energies): warnings.warn( 'nframes %d in xdat is larger than energy %d, use the last %d frames' % (xdat_nframes, len(energies), len(energies))) xdat_nlines = len(energies) * xdat_secsize xdat_lines = xdat_lines[xdat_nlines:] xdat_nframes = len(xdat_lines) // xdat_secsize print(xdat_nframes, len(energies)) # loop over frames for ii in range(xdat_nframes): frame_path = 'frame.%06d' % ii os.makedirs(frame_path, exist_ok=True) os.chdir(frame_path) # clear dir for jj in ['conf.lmp']: if os.path.isfile(jj): os.remove(jj) for jj in ['lammps.in'] + meam_potfile_name: if os.path.islink(jj): os.unlink(jj) # link lammps in os.symlink(os.path.relpath(f_lammps_in), 'lammps.in') # make conf with open('POSCAR', 'w') as fp: fp.write('\n'.join(xdat_lines[ii * xdat_secsize:(ii + 1) * xdat_secsize])) lammps.cvt_lammps_conf('POSCAR', 'conf.lmp') ptypes = vasp.get_poscar_types('POSCAR') lammps.apply_type_map('conf.lmp', type_map, ptypes) # link models for (kk, ll) in zip(meam_potfile, meam_potfile_name): os.symlink(os.path.relpath(kk), ll) os.chdir(ls) os.chdir(cwd)
def make_deepmd_lammps (jdata, conf_dir) : deepmd_model_dir = jdata['deepmd_model_dir'] deepmd_type_map = jdata['deepmd_type_map'] ntypes = len(deepmd_type_map) deepmd_model_dir = os.path.abspath(deepmd_model_dir) deepmd_models = glob.glob(os.path.join(deepmd_model_dir, '*pb')) deepmd_models_name = [os.path.basename(ii) for ii in deepmd_models] vol_start = jdata['vol_start'] vol_end = jdata['vol_end'] vol_step = jdata['vol_step'] # # get equi props # equi_path = re.sub('confs', global_equi_name, conf_path) # equi_path = os.path.join(equi_path, 'lmp') # equi_log = os.path.join(equi_path, 'log.lammps') # if not os.path.isfile(equi_log) : # raise RuntimeError("the system should be equilibriated first") # natoms, epa, vpa = lammps.get_nev(equi_log) # task path task_path = re.sub('confs', global_task_name, conf_dir) task_path = os.path.abspath(task_path) os.makedirs(task_path, exist_ok = True) cwd = os.getcwd() conf_path = os.path.abspath(conf_dir) from_poscar = os.path.join(conf_path, 'POSCAR') to_poscar = os.path.join(task_path, 'POSCAR') if os.path.exists(to_poscar) : assert(filecmp.cmp(from_poscar, to_poscar)) else : os.chdir(task_path) os.symlink(os.path.relpath(from_poscar), 'POSCAR') os.chdir(cwd) volume = vasp.poscar_vol(to_poscar) natoms = vasp.poscar_natoms(to_poscar) vpa = volume / natoms # structrure ss = Structure.from_file(to_poscar) # lmp path lmp_path = os.path.join(task_path, 'deepmd') os.makedirs(lmp_path, exist_ok = True) # # lmp conf # conf_file = os.path.join(lmp_path, 'conf.lmp') # lammps.cvt_lammps_conf(to_poscar, conf_file) # ptypes = vasp.get_poscar_types(to_poscar) # lammps.apply_type_map(conf_file, deepmd_type_map, ptypes) for vol in np.arange(vol_start, vol_end, vol_step) : vol_path = os.path.join(lmp_path, 'vol-%.2f' % vol) print('# generate %s' % (vol_path)) os.makedirs(vol_path, exist_ok = True) os.chdir(vol_path) #print(vol_path) for ii in ['conf.lmp', 'conf.lmp'] + deepmd_models_name : if os.path.exists(ii) : os.remove(ii) # # link conf # os.symlink(os.path.relpath(conf_file), 'conf.lmp') # make conf scale_ss = ss.copy() scale_ss.scale_lattice(vol * natoms) scale_ss.to('POSCAR', 'POSCAR') lammps.cvt_lammps_conf('POSCAR', 'conf.lmp') ptypes = vasp.get_poscar_types('POSCAR') lammps.apply_type_map('conf.lmp', deepmd_type_map, ptypes) # link models for (ii,jj) in zip(deepmd_models, deepmd_models_name) : os.symlink(os.path.relpath(ii), jj) # make lammps input scale = (vol / vpa) ** (1./3.) fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, scale,lammps.inter_deepmd, deepmd_models_name) with open(os.path.join(vol_path, 'lammps.in'), 'w') as fp : fp.write(fc) os.chdir(cwd)
def make_meam_lammps_fixv (jdata, conf_dir) : meam_potfile_dir = jdata['meam_potfile_dir'] meam_potfile_dir = os.path.abspath(meam_potfile_dir) meam_potfile = jdata['meam_potfile'] meam_potfile = [os.path.join(meam_potfile_dir,ii) for ii in meam_potfile] meam_potfile_name = jdata['meam_potfile'] type_map = jdata['meam_type_map'] ntypes = len(type_map) meam_param = {'meam_potfile' : jdata['meam_potfile'], 'meam_type': jdata['meam_param_type']} vol_start = jdata['vol_start'] vol_end = jdata['vol_end'] vol_step = jdata['vol_step'] # get equi props equi_path = re.sub('confs', global_equi_name, conf_dir) task_path = re.sub('confs', global_task_name, conf_dir) equi_path = os.path.join(equi_path, 'meam') task_path = os.path.join(task_path, 'meam') equi_path = os.path.abspath(equi_path) task_path = os.path.abspath(task_path) equi_log = os.path.join(equi_path, 'log.lammps') equi_dump = os.path.join(equi_path, 'dump.relax') os.makedirs(task_path, exist_ok = True) task_poscar = os.path.join(task_path, 'POSCAR') lammps.poscar_from_last_dump(equi_dump, task_poscar, type_map) cwd = os.getcwd() volume = vasp.poscar_vol(task_poscar) natoms = vasp.poscar_natoms(task_poscar) vpa = volume / natoms # structrure ss = Structure.from_file(task_poscar) # make lammps.in fc = lammps.make_lammps_equi('conf.lmp', ntypes, lammps.inter_meam, meam_param, change_box = False) f_lammps_in = os.path.join(task_path, 'lammps.in') with open(f_lammps_in, 'w') as fp : fp.write(fc) # make vols for vol in np.arange(vol_start, vol_end, vol_step) : vol_path = os.path.join(task_path, 'vol-%.2f' % vol) print('# generate %s' % (vol_path)) os.makedirs(vol_path, exist_ok = True) os.chdir(vol_path) for ii in ['conf.lmp', 'conf.lmp', 'lammps.in'] + meam_potfile_name : if os.path.exists(ii) : os.remove(ii) # make conf scale_ss = ss.copy() scale_ss.scale_lattice(vol * natoms) scale_ss.to('POSCAR', 'POSCAR') lammps.cvt_lammps_conf('POSCAR', 'conf.lmp') ptypes = vasp.get_poscar_types('POSCAR') lammps.apply_type_map('conf.lmp', type_map, ptypes) # link lammps.in os.symlink(os.path.relpath(f_lammps_in), 'lammps.in') # link models for (ii,jj) in zip(meam_potfile, meam_potfile_name) : os.symlink(os.path.relpath(ii), jj) # make lammps input os.chdir(cwd)