From dc6055bb1856f145908043a6ee4dedd11a850465 Mon Sep 17 00:00:00 2001 From: Qiangqiang Gu <98570179+QG-phy@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:48:59 +0800 Subject: [PATCH] Add Wannier TB for initial training SK TB parameters. (#41) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📃 docs((train): update comments 1. remove useless comments in train_nnsk 2. add comment in trainloss for block_l2) * feat: update using wannier as training labels * feat: updata trainloss.py the block_l2 remove the randdom rate for calc loss * 🧪 test: create test_process_wannier.py * ✨ feat(dataprocess): update process_wannier.py: add the onsite energy shift. shift the min onsite energy of wanierTB to make it aligned with onsite db values. * update process_wannier.py * test: update hop_bondwise.npy and test_process_wannier.py * example: add example of using wannier as training labels on Si * test: add test for train using wannier TB as labels. * ci: update unit_test.yml * ✨ feat(dataload argcheck): add new tag for wannier add a para. in data_options. use_wannier. to set whether to use wannier as labels. * test: update input_wan.json for tests * update docstring process_wannier.py * Stop tracking and remove tests/data/hBN/data/set.0/xdat2.traj * chore: delete xdat2.traj * Stop tracking and remove dptb/tests/data/postrun/run_config.json * Stop tracking and remove dptb/tests/data/test_all/test_config.json * Stop tracking and remove /tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json tests/data/test_all/fancy_ones/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json --------- Co-authored-by: Yinzhanghao Zhou <64253517+floatingCatty@users.noreply.github.com> --- .github/workflows/unit_test.yml | 2 +- .gitignore | 6 + dptb/dataprocess/datareader.py | 36 +- dptb/dataprocess/process_wannier.py | 266 + dptb/nnops/train_nnsk.py | 14 - dptb/nnops/trainloss.py | 13 +- dptb/tests/data/postrun/run_config.json | 21 - .../best_nnsk_b4.000_c4.000_w0.300.json | 57 - .../best_nnsk_b4.000_c4.000_w0.300.json | 302 - dptb/tests/data/test_all/test_config.json | 100 - dptb/tests/data/wan/Rlatt.npy | Bin 0 -> 20696 bytes dptb/tests/data/wan/hop_bondwise.npy | Bin 0 -> 700107 bytes dptb/tests/data/wan/hopps.npy | Bin 0 -> 877696 bytes dptb/tests/data/wan/input_wan.json | 59 + dptb/tests/data/wan/silicon.vasp | 10 + dptb/tests/data/wan/wannier90_hr.dat | 54909 ++++++++++++++++ dptb/tests/test_all.py | 17 + dptb/tests/test_process_wannier.py | 86 + dptb/utils/argcheck.py | 28 +- dptb/utils/constants.py | 8 +- examples/Si_wannier/data/POSCAR | 10 + .../Si_wannier/data/kpath_spk.0/bandinfo.json | 7 + examples/Si_wannier/data/kpath_spk.0/eigs.npy | Bin 0 -> 6960 bytes .../Si_wannier/data/kpath_spk.0/kpoints.npy | Bin 0 -> 860 bytes .../data/kpath_spk.0/wannier90_hr.dat | 54909 ++++++++++++++++ .../Si_wannier/data/kpath_spk.0/xdat.traj | Bin 0 -> 430 bytes examples/Si_wannier/data/silicon.vasp | 10 + examples/Si_wannier/data/wannier90_hr.dat | 54909 ++++++++++++++++ examples/Si_wannier/input.json | 59 + examples/Si_wannier/plotband.ipynb | 143 + 30 files changed, 165459 insertions(+), 522 deletions(-) create mode 100644 dptb/dataprocess/process_wannier.py delete mode 100644 dptb/tests/data/postrun/run_config.json delete mode 100644 dptb/tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json delete mode 100644 dptb/tests/data/test_all/fancy_ones/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json delete mode 100644 dptb/tests/data/test_all/test_config.json create mode 100644 dptb/tests/data/wan/Rlatt.npy create mode 100644 dptb/tests/data/wan/hop_bondwise.npy create mode 100644 dptb/tests/data/wan/hopps.npy create mode 100644 dptb/tests/data/wan/input_wan.json create mode 100644 dptb/tests/data/wan/silicon.vasp create mode 100644 dptb/tests/data/wan/wannier90_hr.dat create mode 100644 dptb/tests/test_process_wannier.py create mode 100644 examples/Si_wannier/data/POSCAR create mode 100644 examples/Si_wannier/data/kpath_spk.0/bandinfo.json create mode 100644 examples/Si_wannier/data/kpath_spk.0/eigs.npy create mode 100644 examples/Si_wannier/data/kpath_spk.0/kpoints.npy create mode 100644 examples/Si_wannier/data/kpath_spk.0/wannier90_hr.dat create mode 100644 examples/Si_wannier/data/kpath_spk.0/xdat.traj create mode 100644 examples/Si_wannier/data/silicon.vasp create mode 100644 examples/Si_wannier/data/wannier90_hr.dat create mode 100644 examples/Si_wannier/input.json create mode 100644 examples/Si_wannier/plotband.ipynb diff --git a/.github/workflows/unit_test.yml b/.github/workflows/unit_test.yml index 56010c70..52ec8af6 100644 --- a/.github/workflows/unit_test.yml +++ b/.github/workflows/unit_test.yml @@ -18,7 +18,7 @@ jobs: id: s1 uses: actions/checkout@v3 with: - fetch-depth: 0 + fetch-depth: 0 ref: "refs/pull/${{ github.event.number }}/merge" - name: Install DeePTB & Run Test id: s2 diff --git a/.gitignore b/.gitignore index d8a60ceb..2d3909b8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,10 @@ test*.ipynb +dptb/tests/data/hBN/data/set.0/xdat2.traj +dptb/tests/data/postrun/run_config.json +dptb/tests/data/test_all/test_config.json +dptb/tests/data/test_all/checkpoint/best_nnsk_b5.000_c6.615_w0.265.json +dptb/tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json +dptb/tests/data/test_all/fancy_ones/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json dptb/nnet/__pycache__/ dptb/sktb/__pycache__/ dptb/negf/__pycache__/ diff --git a/dptb/dataprocess/datareader.py b/dptb/dataprocess/datareader.py index d4ccb228..887281a4 100644 --- a/dptb/dataprocess/datareader.py +++ b/dptb/dataprocess/datareader.py @@ -6,10 +6,11 @@ import torch from dptb.structure.structure import BaseStruct from dptb.dataprocess.processor import Processor +from dptb.dataprocess.process_wannier import get_wannier_blocks from dptb.utils.tools import j_loader from dptb.utils.argcheck import normalize_bandinfo -def read_data(path, prefix, cutoff, proj_atom_anglr_m, proj_atom_neles, onsitemode:str='uniform', time_symm=True, **kwargs): +def read_data(path, prefix, cutoff, proj_atom_anglr_m, proj_atom_neles, onsitemode:str='uniform', time_symm=True, use_wannier=False, **kwargs): """根据文件路径和prefix的读取文件夹下的数据文件,并存储为神经网络模型的输入格式数据 """ filenames = { @@ -17,7 +18,7 @@ def read_data(path, prefix, cutoff, proj_atom_anglr_m, proj_atom_neles, onsitemo "eigen_file": "eigs.npy", "kpoints_file" : "kpoints.npy", "bandinfo_file": "bandinfo.json", - "wannier_file": "wannier.npy" + "wannier_file": "wannier90_hr.dat" } filenames.update(kwargs) @@ -38,12 +39,7 @@ def read_data(path, prefix, cutoff, proj_atom_anglr_m, proj_atom_neles, onsitemo kpoints = np.load(data_dirs[ii] + "/" + filenames['kpoints_file']) eigs = np.load(data_dirs[ii] + "/" + filenames['eigen_file']) bandinfo = j_loader(data_dirs[ii] + "/" + filenames['bandinfo_file']) - if os.path.exists(data_dirs[ii] + "/" + filenames['wannier_file']): - wannier = np.load(data_dirs[ii] + "/" + filenames['wannier_file'], allow_pickle=True) - wannier = [x.tolist() for x in wannier] - else: - wannier = [None] - + bandinfo = normalize_bandinfo(bandinfo) bandinfo_sets.append(bandinfo) if len(eigs.shape)==2: @@ -51,18 +47,28 @@ def read_data(path, prefix, cutoff, proj_atom_anglr_m, proj_atom_neles, onsitemo assert len(eigs.shape) == 3 kpoints_sets.append(kpoints) eigens_sets.append(eigs) - if wannier[0] is None: - wannier = [None] * eigs.shape[0] - wannier_sets.append(wannier) - - - for iatom in asetrajs: + for iatom in asetrajs: struct = BaseStruct(atom=iatom, format='ase', cutoff=cutoff, proj_atom_anglr_m=proj_atom_anglr_m, proj_atom_neles=proj_atom_neles, onsitemode=onsitemode, time_symm=time_symm) struct_list.append(struct) struct_list_sets.append(struct_list) - + if use_wannier: + assert os.path.exists(data_dirs[ii] + "/" + filenames['wannier_file']) + #wannier = np.load(data_dirs[ii] + "/" + filenames['wannier_file'], allow_pickle=True) + assert len(struct_list) == 1, "wannier90_hr.dat should be calculated for one structure only!" + wannier_proj = bandinfo['wannier_proj'] + orb_wan = bandinfo.get('orb_wan', None) + wannier = get_wannier_blocks(file=data_dirs[ii] + "/" + filenames['wannier_file'], + struct=struct_list[0], wannier_proj_orbital=wannier_proj,orb_wan=orb_wan) + wannier = [wannier] + else: + wannier = [None] + + if wannier[0] is None: + wannier = [None] * eigs.shape[0] + wannier_sets.append(wannier) + return struct_list_sets, kpoints_sets, eigens_sets, bandinfo_sets, wannier_sets diff --git a/dptb/dataprocess/process_wannier.py b/dptb/dataprocess/process_wannier.py new file mode 100644 index 00000000..1cbf713e --- /dev/null +++ b/dptb/dataprocess/process_wannier.py @@ -0,0 +1,266 @@ +import re +import numpy as np +from ase import Atoms +from dptb.utils.constants import anglrMId, Orbital_Order_Wan_Default, Orbital_Order_SK +from dptb.structure.structure import BaseStruct +from dptb.nnsktb.onsiteDB import onsite_energy_database + +def get_wannier_blocks(file:str, struct:BaseStruct, wannier_proj_orbital:dict, orb_wan:dict=None): + """ get the hopping matrices in the order of dptb. + """ + + Rlatt, hopps, indR0 = read_hr(file) + wannier_orbital_order, sk_orbital_order, iatom_nors = wan_orbital_orders(struct, wannier_proj_orbital, orb_wan) + hopping_bonds = transfrom_Hwan(hopps, Rlatt, indR0, struct, wannier_orbital_order, sk_orbital_order, iatom_nors) + + return hopping_bonds + + +def read_hr(file='wannier90_hr.dat'): + """ Read wannier90_hr.dat. The wannier TB parameters files are generated by wannier90.x. + + Parameters: + ---------- + file: filename of wannier90_hr.dat + + Returns: + -------- + Rlatt: lattice vectors of Wigner-Seitz grid points, shape (nrpts,3) + hopps: hopping matrices, shape (nrpts,num_wann,num_wann) + indR0: index of R=0, where the hopps[indR0] corresponding to the onsite Hamiltonian block. + + """ + + f=open(file,'r') + data=f.readlines() + #read hopping matrix + num_wann = int(data[1]) + nrpts = int(data[2]) + r_hop= np.zeros([num_wann,num_wann], dtype=complex) + #hop=[] + #skip n lines of degeneracy of each Wigner-Seitz grid point + skiplines = int(np.ceil(nrpts / 15.0)) + istart = 3 + skiplines + deg=[] + for i in range(3,istart): + deg.append(np.array([int(j) for j in data[i].split()])) + deg=np.concatenate(deg,0) + + icount=0 + ii=-1 + Rlatt = [] + hopps = [] + for i in range(istart,len(data)): + line=data[i].split() + m = int(line[3]) - 1 + n = int(line[4]) - 1 + r_hop[m,n] = complex(round(float(line[5]),6),round(float(line[6]),6)) + icount+=1 + if(icount % (num_wann*num_wann) == 0): + ii+=1 + R = np.array([float(x) for x in line[0:3]]) + Rlatt.append(R) + hopps.append(r_hop) + r_hop= np.zeros([num_wann,num_wann], dtype=complex) + Rlatt=np.asarray(Rlatt,dtype=int) + hopps=np.asarray(hopps) + deg = np.reshape(deg,[nrpts,1,1]) + hopps=hopps/deg + + for i in range(nrpts): + if (Rlatt[i]==0).all(): + indR0 = i + + return Rlatt, hopps, indR0 + + +def wan_orbital_orders(struct:BaseStruct, wannier_proj_orbital:dict, orb_wan:dict=None): + """ get the wannier orbital orders for the wannier orbitals in wannier90_hr.dat. + by default is shoule be in the order of : + atom-0-s, atom-0-pz,atom-0-px, atom-0-py, atom-1-s, atom-1-pz, ..., etc. + + Parameters: + ----------- + structase: ase.Atoms + wannier_proj_orbital: dict,the orbital defined in projection in wannierizaion process. + e.g.: {'N': ['s','p'], 'B': ['s']} or {'N': 'p', 'B': 's'} + orb_wan: dict, the orbital order in wannier90_hr.dat. + e.g.: {'s': ['s'], 'p': ['pz','px','py'], 'd': ['dz2','dxz','dyz','dx2-y2','dxy']} + + Returns: + -------- + wannier_orbital_order: list, the orbital order in wannier90_hr.dat. + sk_orbital_order: list, the orbital order in dptb. + iatom_nors: list, the number of orbitals on every atom. + + """ + + proj_atom_anglr_m = struct.proj_atom_anglr_m + if orb_wan is None: + orb_wan = Orbital_Order_Wan_Default + orb_sk = Orbital_Order_SK + + # take the projected_struct as from the structure class, which should be Atoms object. + projected_struct = struct.projected_struct + assert isinstance(projected_struct,Atoms), 'projected_struct should be ase.Atoms' + # check the consistency of wannier_proj_orbital and proj_atom_anglr_m. + assert set(wannier_proj_orbital.keys()) == set(proj_atom_anglr_m.keys()) + for ii in proj_atom_anglr_m: + assert len(wannier_proj_orbital[ii]) == len(proj_atom_anglr_m[ii]), 'proj_atom_anglr_m and wannier_proj_orbital are not consistent' + for iorb in proj_atom_anglr_m[ii]: + ishell_symbol = ''.join(re.findall(r'[A-Za-z]',iorb)) + assert ishell_symbol in wannier_proj_orbital[ii], 'proj_atom_anglr_m and wannier_proj_orbital are not consistent' + + # ------------------------------ + # get the wannier orbitals in order. + # ------------------------------ + # wannier_orbital_order: the wannier orbitals in the order of wannier90_hr.dat + # e.g.: ['0-s','0-pz','0-px', ... ] + # sk_orbital_order: the orbitals in the order of dptb. + # iatom_nors: the total number of orbitals on every atom. + # ------------------------------ + + iatom_nors = [] # number of atoms on every atoms: + wannier_orbital_order = [] + sk_orbital_order = [] + + projected_struct_symbols = projected_struct.get_chemical_symbols() # list of atom symbols in the projected_struct + for ia in range(len(projected_struct_symbols)): + iatom_symbols = projected_struct_symbols[ia] # atom symbol of the ia-th atom in the projected_struct + ii_num_orbs = 0 + if isinstance (wannier_proj_orbital[iatom_symbols],list): + iorblist = wannier_proj_orbital[iatom_symbols] + elif isinstance (wannier_proj_orbital[iatom_symbols],str): + iorblist = [wannier_proj_orbital[iatom_symbols]] + else: + raise ValueError('wannier_proj_orbital should be a list or a string') + + for iorb in iorblist: + ii_num_orbs += 2 * anglrMId[iorb] + 1 + for ii_orb in orb_wan[iorb]: + wannier_orbital_order.append(f'{ia}-{ii_orb}') + + iatom_nors.append(ii_num_orbs) + + if isinstance (proj_atom_anglr_m[iatom_symbols],list): + iorblist = proj_atom_anglr_m[iatom_symbols] + elif isinstance (proj_atom_anglr_m[iatom_symbols],str): + iorblist = [proj_atom_anglr_m[iatom_symbols]] + else: + raise ValueError('proj_atom_anglr_m should be a list or a string') + + for iorb in iorblist: + ishell_symbol = ''.join(re.findall(r'[A-Za-z]',iorb)) + for ii_orb in orb_sk[ishell_symbol]: + sk_orbital_order.append(f'{ia}-{ii_orb}') + + assert len(wannier_orbital_order) == len(wannier_orbital_order), 'wannier_orb_in and sk_orb_in are not consistent' + assert set(wannier_orbital_order) == set(wannier_orbital_order), 'wannier_orb_in and sk_orb_in are not consistent' + + iatom_nors=np.array(iatom_nors,dtype=int) + + return wannier_orbital_order, sk_orbital_order, iatom_nors + +def get_onsite_shift(hopps_r00, struct, wannier_orbital_order, unit='eV'): + '''The function `get_onsite_shift` calculates the onsite shift of a given orbital in a crystal + structure based on the hopping matrix elements in wannier and a database of onsite energies. + + Parameters + ---------- + hopps_r00 + The variable `hopps_r00` represents the onsite Hamiltonian matrix elements. It is a square matrix + where each element represents the interaction energy between two orbitals on the same atom. + + struct + The `struct` parameter is an object that represents the structure of the system. It likely contains + information about the positions of atoms in the system and other relevant properties. + + wannier_orbital_order + The `wannier_orbital_order` parameter is a list that specifies the order of the Wannier orbitals. + Each element in the list represents a Wannier orbital and is in the format + "atom_index-orbital_symbol". For example, if there are 3 atoms and + + unit, optional + The `unit` parameter specifies the unit in which the onsite shift will be calculated. It can take + one of three values: 'eV', 'Ry', or 'Hartree'. + + Returns + ------- + the value of the onsite shift, which is calculated based on the input parameters. + + ''' + + projected_struct = struct.projected_struct + projected_struct_symbols = projected_struct.get_chemical_symbols() # list of atom symbols in the projected_struct + + onsite_diag_elements = dict(zip(wannier_orbital_order, np.diag(hopps_r00).real)) + min_key = min(onsite_diag_elements, key=onsite_diag_elements.get) + atom_ind = int(min_key.split('-')[0]) + orb_symbol = min_key.split('-')[1][0] + atom_symbol = projected_struct_symbols[atom_ind] + + proj_atom_anglr_m = struct.proj_atom_anglr_m + + if unit == 'eV': + factor = 13.605662285137 * 2 # Hartree to eV + elif unit == 'Ry': + factor = 2.0 # Hartree to Ry + elif unit == 'Hartree': + factor = 1.0 + else: + raise ValueError('unit must be eV, Ry or Hartree') + + onsite_e_db={} + for i in proj_atom_anglr_m: + onsite_e_db[i]={} + for iorb in proj_atom_anglr_m[i]: + ishell_symbol = ''.join(re.findall(r'[A-Za-z]',iorb)) + onsite_e_db[i][ishell_symbol] = onsite_energy_database[i][iorb] * factor + + database_onsite_e_min = onsite_e_db[atom_symbol][orb_symbol] + + onsite_shift = onsite_diag_elements[min_key] - database_onsite_e_min + + return onsite_shift + +def transfrom_Hwan(hopps, Rlatt, indR0, struct, wannier_orbital_order, sk_orbital_order, iatom_nors): + """ transform the hopping matrices from the order of wannier90_hr.dat to the order of dptb. + + Parameters: + ----------- + hopps: hopping matrices, shape (nrpts,num_wann,num_wann) + Rlatt: lattice vectors of Wigner-Seitz grid points, shape (nrpts,3) + wannier_orbital_order: list, the orbital order in wannier90_hr.dat. + sk_orbital_order: list, the orbital order in dptb. + iatom_nors: list, the number of orbitals on every atom. + + Returns: + -------- + hopping_bonds: dict, the hopping matrices in the order of dptb. + e.g.: hopping_bonds = {'0_0_0_0_0': H_0,0 block at R=000, '0_1_0_0_1': H_0,1 block at R=001, ... } + """ + + norb = len(sk_orbital_order) + Mateye = np.eye(norb,dtype=int) + mtrans = np.zeros([norb,norb],dtype=int) + for i in range(norb): + iorb = sk_orbital_order[i] + assert iorb in wannier_orbital_order + ind = wannier_orbital_order.index(iorb) + mtrans[i] += Mateye[ind] + + onsite_shift = get_onsite_shift(hopps[indR0], struct, wannier_orbital_order, unit='eV') + + hopps_skorder = mtrans @ hopps @ mtrans.T + hopps_skorder[indR0] = hopps_skorder[indR0] - onsite_shift * np.eye(norb) + + hopping_bonds = {} + for ir in range(len(Rlatt)): + iR = Rlatt[ir] + for ia in range(len(iatom_nors)): + ist, ied = (np.sum(iatom_nors[:ia]),np.sum(iatom_nors[:ia+1])) + for ja in range(len(iatom_nors)): + jst, jed = (np.sum(iatom_nors[:ja]),np.sum(iatom_nors[:ja+1])) + hopping_bonds[f'{ia}_{ja}_{iR[0]}_{iR[1]}_{iR[2]}'] = hopps_skorder[ir,ist:ied,jst:jed].real + + return hopping_bonds \ No newline at end of file diff --git a/dptb/nnops/train_nnsk.py b/dptb/nnops/train_nnsk.py index d07b77c7..9fb73649 100644 --- a/dptb/nnops/train_nnsk.py +++ b/dptb/nnops/train_nnsk.py @@ -155,7 +155,6 @@ def calc(self, batch_bonds, batch_bond_onsites, batch_envs, batch_onsitenvs, str if self.soc: self.soc_coeff = nn_soc_lambdas - # constructing hamiltonians and decomposition pred = [] label = [] @@ -163,15 +162,11 @@ def calc(self, batch_bonds, batch_bond_onsites, batch_envs, batch_onsitenvs, str l = [] if self.onsitemode == 'strain': onsiteEs, onsiteVs, hoppings = batch_onsiteEs[ii], batch_onsiteVs[ii], batch_hoppings[ii] - # TODO: 这里的numpy 是否要改为tensor 方便之后为了GPU的加速。 - # onsitenvs = np.asarray(batch_onsitenvs[ii][:,1:]) onsitenvs = batch_onsitenvs[ii][:,1:] - # call hamiltonian block else: onsiteEs, hoppings = batch_onsiteEs[ii], batch_hoppings[ii] onsiteVs = None onsitenvs = None - # call hamiltonian block if self.overlap: overlaps = batch_overlaps[ii] else: @@ -182,8 +177,6 @@ def calc(self, batch_bonds, batch_bond_onsites, batch_envs, batch_onsitenvs, str else: soc_lambdas = None - # bond_onsites = np.asarray(batch_bond_onsites[ii][:,1:]) - # bond_hoppings = np.asarray(batch_bonds[ii][:,1:]) bond_onsites = batch_bond_onsites[ii][:,1:] bond_hoppings = batch_bonds[ii][:,1:] @@ -192,8 +185,6 @@ def calc(self, batch_bonds, batch_bond_onsites, batch_envs, batch_onsitenvs, str bonds_hoppings=bond_hoppings, onsite_envs=onsitenvs) if decompose: - #if self.run_opt["freeze"]: - # kpoints = np.array([[0,0,0]]) eigenvalues_ii, _ = self.hamileig.Eigenvalues(kpoints=kpoints, time_symm=self.common_options["time_symm"], unit=self.common_options["unit"]) pred.append(eigenvalues_ii) else: @@ -208,9 +199,6 @@ def calc(self, batch_bonds, batch_bond_onsites, batch_envs, batch_onsitenvs, str label.append(l) if decompose: - #if self.run_opt["freeze"]: - # label = torch.from_numpy(eigenvalues.astype(float))[:,[0],:].float() - #else: label = torch.from_numpy(eigenvalues.astype(float)).float() pred = torch.stack(pred) return pred, label @@ -280,8 +268,6 @@ def validation(self, **kwargs): eigenvalues_pred, eigenvalues_lbl = self.calc(*data) total_loss += self.validation_lossfunc(eig_pred=eigenvalues_pred,eig_label=eigenvalues_lbl,**self.validation_loss_options) - #total_loss += loss_type1(self.criterion, eigenvalues_pred, eigenvalues_lbl, num_el, num_kp, - # self.band_min, self.band_max) if kwargs.get('quick'): break with torch.enable_grad(): diff --git a/dptb/nnops/trainloss.py b/dptb/nnops/trainloss.py index 3a181b26..ec6ad9b5 100644 --- a/dptb/nnops/trainloss.py +++ b/dptb/nnops/trainloss.py @@ -247,22 +247,23 @@ def loss_proj_env(self, eig_pred, eig_label, ev_pred, proj_label, band_min=0, ba return loss def block_l2(self, pred, label, **kwargs): - """wannier fixing loss function, just a deviation function of two list + """Hamiltonian block as training labels, such as wannier TB hamiltonians. The loss the is defined as deviation function of two list of tensors. Args: pred (list(torch.Tensor)): predicted hamiltonian blocks - label (list(torch.Tensor)): wannier hamiltonian blocks + label (list(torch.Tensor)): labeled hamiltonian blocks e.g. Wannier TB hamiltonians """ assert len(pred) == len(label) loss = 0 - count = 0 + count = 1 for st in range(len(pred)): for p, l in zip(pred[st], label[st]): rd = np.random.randint(low=0, high=10) - if rd >= 3: - loss += self.criterion(l, p) - count += 1 + # only calculate loss for 70% of the blocks + #if rd >= 3: + loss += self.criterion(l, p) + count += 1 return loss / count diff --git a/dptb/tests/data/postrun/run_config.json b/dptb/tests/data/postrun/run_config.json deleted file mode 100644 index cb440fbf..00000000 --- a/dptb/tests/data/postrun/run_config.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "structure": "./dptb/tests/data/hBN/hBN.vasp", - "task_options": { - "task": "band", - "kline_type": "ase", - "kpath": "GMKG", - "nkpoints": 120, - "emin": -10, - "emax": 10, - "klabels": [ - "" - ], - "E_fermi": null, - "ref_band": null - }, - "init_model": { - "path": "/root/deeptb/dptb/tests/data/hBN/checkpoint/best_dptb.pth", - "interpolate": false - }, - "use_correction": null -} \ No newline at end of file diff --git a/dptb/tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json b/dptb/tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json deleted file mode 100644 index 13d49752..00000000 --- a/dptb/tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json +++ /dev/null @@ -1,57 +0,0 @@ -{ - "onsite": {}, - "hopping": { - "N-N-2s-2s-0": [ - 0.0315699465572834, - -0.0035687200725078583 - ], - "N-N-2s-2p-0": [ - -0.03446169197559357, - 0.002320951782166958 - ], - "N-N-2p-2p-0": [ - 0.02015334740281105, - -0.018557660281658173 - ], - "N-N-2p-2p-1": [ - 0.009781434200704098, - -0.03129514306783676 - ], - "N-B-2s-2s-0": [ - 0.03516198694705963, - 0.004189460538327694 - ], - "N-B-2s-2p-0": [ - -0.03589026257395744, - -0.010575673542916775 - ], - "N-B-2p-2s-0": [ 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b/dptb/tests/test_all.py @@ -18,6 +18,9 @@ INPUT_nnsk_nrl_test = os.path.join(Path(os.path.abspath(__file__)).parent, "data/nrl/input_nrl_test.json") ckpt_nnsk_nrl_path = os.path.join(Path(os.path.abspath(__file__)).parent, "../../examples/NRL-TB/silicon/ckpt") +INPUT_nnsk_wan = os.path.join(Path(os.path.abspath(__file__)).parent, "data/wan/input_wan.json") + + log = logging.getLogger(__name__) @@ -50,6 +53,20 @@ def test_train_sk(): use_correction=False, ) +def test_train_wan(): + train( + INPUT = INPUT_nnsk_wan, + init_model = None, + restart=None, + freeze=False, + train_soc=False, + output=test_data_path+"/test_all", + log_level=2, + log_path=None, + train_sk=False, + use_correction=False, + ) + def test_train_init_model(): train( INPUT=INPUT, diff --git a/dptb/tests/test_process_wannier.py b/dptb/tests/test_process_wannier.py new file mode 100644 index 00000000..a862aece --- /dev/null +++ b/dptb/tests/test_process_wannier.py @@ -0,0 +1,86 @@ +import numpy as np +import torch +import pytest +from dptb.dataprocess.process_wannier import read_hr, wan_orbital_orders, transfrom_Hwan, get_onsite_shift +from dptb.structure.structure import BaseStruct + +@pytest.fixture(scope='session', autouse=True) +def root_directory(request): + """ + :return: + """ + return str(request.config.rootdir) + + +def test_read_hr(root_directory): + # Test 1.1: Read a single wannier90_hr.dat file + wannierfile = root_directory + '/dptb/tests/data/wan/wannier90_hr.dat' + Rlatt, hopps, indR0 = read_hr(wannierfile) + + Rlatt_true = np.load(root_directory + '/dptb/tests/data/wan/Rlatt.npy') + hopps_true = np.load(root_directory + '/dptb/tests/data/wan/hopps.npy') + + assert indR0 == 428 + assert Rlatt.shape == (857, 3) + assert hopps.shape == (857, 8, 8) + assert np.allclose(Rlatt, Rlatt_true) + assert np.allclose(hopps, hopps_true) + +def test_wan_orbital_orders(root_directory): + + proj_atom_anglr_m = {'Si':['3s','3p']} + proj_atom_neles={'Si':4} + CutOff = 2.5 + struct =BaseStruct(atom=root_directory + '/dptb/tests/data/wan/silicon.vasp',format='vasp', + cutoff=CutOff,proj_atom_anglr_m=proj_atom_anglr_m,proj_atom_neles=proj_atom_neles, onsitemode='uniform') + + wannier_orbital_order, sk_orbital_order, iatom_nors = wan_orbital_orders(struct=struct, wannier_proj_orbital={'Si':['s','p']}) + + wan_orb_order_true = ['0-s', '0-pz', '0-px', '0-py', '1-s', '1-pz', '1-px', '1-py'] + sk_orb_order_true = ['0-s', '0-py', '0-pz', '0-px', '1-s', '1-py', '1-pz', '1-px'] + + + assert wannier_orbital_order == wan_orb_order_true + assert sk_orbital_order == sk_orb_order_true + assert np.allclose(iatom_nors, np.array([4,4])) + +def test_onsite_shift(root_directory): + hopps = np.load(root_directory + '/dptb/tests/data/wan/hopps.npy') + indR0 = 428 + hopps_r00 = hopps[indR0] + + proj_atom_anglr_m = {'Si':['3s','3p']} + proj_atom_neles={'Si':4} + CutOff = 2.5 + struct =BaseStruct(atom=root_directory + '/dptb/tests/data/wan/silicon.vasp',format='vasp', + cutoff=CutOff,proj_atom_anglr_m=proj_atom_anglr_m,proj_atom_neles=proj_atom_neles, onsitemode='uniform') + + wannier_orbital_order, sk_orbital_order, iatom_nors = wan_orbital_orders(struct=struct, wannier_proj_orbital={'Si':['s','p']}) + + + onsite_shift = get_onsite_shift(hopps_r00, struct, wannier_orbital_order, unit='eV') + + assert abs(onsite_shift - 13.448753996967032) < 1e-8 + +def test_transfrom_Hwan(root_directory): + proj_atom_anglr_m = {'Si':['3s','3p']} + proj_atom_neles={'Si':4} + CutOff = 2.5 + struct =BaseStruct(atom=root_directory + '/dptb/tests/data/wan/silicon.vasp',format='vasp', + cutoff=CutOff,proj_atom_anglr_m=proj_atom_anglr_m,proj_atom_neles=proj_atom_neles, onsitemode='uniform') + + wannier_orbital_order, sk_orbital_order, iatom_nors = wan_orbital_orders(struct=struct, wannier_proj_orbital={'Si':['s','p']}) + + Rlatt = np.load(root_directory + '/dptb/tests/data/wan/Rlatt.npy') + hopps = np.load(root_directory + '/dptb/tests/data/wan/hopps.npy') + indR0 = 428 + hopping_bonds = transfrom_Hwan(hopps, Rlatt, indR0, struct, wannier_orbital_order, sk_orbital_order, iatom_nors) + + hopping_bonds_true = np.load(root_directory + '/dptb/tests/data/wan/hop_bondwise.npy', allow_pickle=True).tolist() + + assert isinstance(hopping_bonds, dict) + + assert hopping_bonds_true.keys() == hopping_bonds.keys() + for ikey in hopping_bonds_true.keys(): + assert np.allclose(hopping_bonds_true[ikey], hopping_bonds[ikey]) + diff --git a/dptb/utils/argcheck.py b/dptb/utils/argcheck.py index 59b27e91..dded8dd2 100644 --- a/dptb/utils/argcheck.py +++ b/dptb/utils/argcheck.py @@ -1,6 +1,8 @@ from typing import List, Callable from dargs import dargs, Argument, Variant, ArgumentEncoder +import logging +log = logging.getLogger(__name__) nnsk_model_config_checklist = ['unit','skfunction-skformula'] nnsk_model_config_updatelist = ['sknetwork-sk_hop_nhidden', 'sknetwork-sk_onsite_nhidden', 'sknetwork-sk_soc_nhidden'] @@ -229,10 +231,11 @@ def data_options(): doc_use_reference = "Whether to use a reference dataset that jointly train the model. It acting as a constraint or normalization to make sure the model won't deviate too much from the reference data." args = [Argument("use_reference", bool, optional=False, doc=doc_use_reference), - train_data_sub(), - validation_data_sub(), - reference_data_sub() - ] + Argument("use_wannier",bool, optional=True, default=False, doc="Whether to use wannier90_hr.dat to construct the wannier basis for the reference data. Default: False"), + train_data_sub(), + validation_data_sub(), + reference_data_sub() + ] doc_data_options = "" @@ -380,6 +383,17 @@ def normalize(data): data = base.normalize_value(data) # data = base.normalize_value(data, trim_pattern="_*") base.check_value(data, strict=True) + + # add check loss and use wannier: + + if data['data_options']['use_wannier']: + if not data['loss_options']['losstype'] .startswith("block"): + log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n') + + if data['loss_options']['losstype'] .startswith("block"): + if not data['data_options']['use_wannier']: + log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n") + raise ValueError return data @@ -850,6 +864,8 @@ def normalize_bandinfo(data): doc_loss_gap_eta = "" doc_eout_weight="" doc_weight = "" + doc_wannier_proj = "" + doc_orb_wan = "" args = [ Argument("band_min", int, optional=True, doc=doc_band_min, default=0), @@ -860,7 +876,9 @@ def normalize_bandinfo(data): Argument("fermi_band", int, optional=True, doc=doc_fermi_band,default=0), Argument("loss_gap_eta", float, optional=True, doc=doc_loss_gap_eta, default=0.01), Argument("eout_weight", float, optional=True, doc=doc_eout_weight, default=0.00), - Argument("weight", [int, float, list], optional=True, doc=doc_weight, default=1.) + Argument("weight", [int, float, list], optional=True, doc=doc_weight, default=1.), + Argument("wannier_proj",dict, optional=True, doc=doc_wannier_proj, default={}), + Argument("orb_wan",[dict, None], optional=True, doc=doc_orb_wan, default=None) ] bandinfo = Argument("bandinfo", dict, sub_fields=args) data = bandinfo.normalize_value(data) diff --git a/dptb/utils/constants.py b/dptb/utils/constants.py index 8fd5cebf..c571453b 100644 --- a/dptb/utils/constants.py +++ b/dptb/utils/constants.py @@ -20,8 +20,14 @@ MaxShells = 3 NumHvals = 10 -dtype_dict = {"float32": torch.float32, "float64": torch.float64} +Orbital_Order_Wan_Default = { 's': ['s'], + 'p': ['pz','px','py'], + 'd': ['dz2','dxz','dyz','dx2-y2','dxy']} +Orbital_Order_SK = {'s': ['s'], + 'p': ['py','pz','px'], + 'd': ['dxy','dyz','dz2','dxz','dx2-y2']} +dtype_dict = {"float32": torch.float32, "float64": torch.float64} # k = Boltzmann # k is the Boltzmann constant in old NEGF module Coulomb = 6.24150974e18 # in the unit of eV*Angstrom eV2J = 1.6021766208e-19 # in the unit of J \ No newline at end of file diff --git a/examples/Si_wannier/data/POSCAR b/examples/Si_wannier/data/POSCAR new file mode 100644 index 00000000..0c6c112a --- /dev/null +++ b/examples/Si_wannier/data/POSCAR @@ -0,0 +1,10 @@ +Primitive Cell + 1.000000 + 0.00000000000000 2.71499984016137 2.71499984016137 + 2.71499984016137 0.00000000000000 2.71499984016137 + 2.71499984016137 2.71499984016137 0.00000000000000 + Si + 2 +Direct + 0.2500000000000000 0.2500000000000000 0.2500000000000000 + 0.0000000000000000 0.0000000000000000 0.0000000000000000 diff --git a/examples/Si_wannier/data/kpath_spk.0/bandinfo.json b/examples/Si_wannier/data/kpath_spk.0/bandinfo.json new file mode 100644 index 00000000..0fbfa115 --- /dev/null +++ b/examples/Si_wannier/data/kpath_spk.0/bandinfo.json @@ -0,0 +1,7 @@ +{ + "band_min": 0, + "band_max": 8, + "wannier_proj": {"Si":["s","p"]}, + "emin": null, + "emax": null +} diff --git a/examples/Si_wannier/data/kpath_spk.0/eigs.npy b/examples/Si_wannier/data/kpath_spk.0/eigs.npy new file mode 100644 index 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NN2HRK(apihost=nnskapi, mode='nnsk')\n", + "\n", + "# set the input parameters for band structure calculation.\n", + "# structure: the path of the structure file.\n", + "run_opt={\"structure\":\"./data/silicon.vasp\",\n", + " \"results_path\":\"./\"}\n", + "# jdata: the input parameters for band structure calculation.\n", + "\n", + "jdata={\"kline_type\":\"abacus\",\n", + " \"kpath\":[[0.0000000000, 0.0000000000, 0.0000000000, 50], \n", + " [0.5000000000, 0.0000000000, 0.5000000000, 50], \n", + " [0.6250000000, 0.2500000000, 0.6250000000, 1], \n", + " [0.3750000000, 0.3750000000, 0.7500000000, 50], \n", + " [0.0000000000, 0.0000000000, 0.0000000000, 50], \n", + " [0.5000000000, 0.5000000000, 0.5000000000, 50], \n", + " [0.5000000000, 0.2500000000, 0.7500000000, 50], \n", + " [0.5000000000, 0.0000000000, 0.5000000000, 1 ]\n", + " ],\n", + " \"nkpoints\":51,\n", + " \"klabels\":[\"G\",\"X\",\"X/U\",\"K\",\"G\",\"L\",\"W\",\"X\"],\n", + " \"E_fermi\":-7.5,\n", + " \"emin\":-23,\n", + " \"emax\":12\n", + " }\n", + "# call bandcalc to calculate the band structure.\n", + "bcalc = bandcalc(apiHrk=nnHrk,run_opt=run_opt,jdata=jdata)\n", + "eigenstatus = bcalc.get_bands()\n", + "\n", + "# load the DFT band data.\n", + "#band = np.loadtxt('../data/soc/BANDS_1.dat')\n", + "band = np.load(\"../silicon/data/kpath.0/eigs.npy\")[0]\n", + "# plot figures.\n", + "plt.figure(figsize=(5,5),dpi=100)\n", + "# in DFT band data, the first column is column index, the second is the kpoints, \n", + "# the 3rd column is the eigenvalues of the first band, the 4th column is the eigenvalues of the second band, and so on.\n", + "# Here, the the first 20 bands are core eletronic bands, not fitting in TB model.\n", + "plt.plot(eigenstatus['xlist'][::6], band[::6,:] - np.min(band[:,:]),'ko',ms=4)\n", + "# set the minimum eigenvalue as 0.\n", + "plt.plot(eigenstatus['xlist'], eigenstatus['eigenvalues']- np.min(eigenstatus['eigenvalues']), 'r-',lw=1)\n", + "\n", + "plt.ylim(-1,20)\n", + "for ii in eigenstatus['high_sym_kpoints']:\n", + " plt.axvline(ii,color='gray',lw=1,ls='--')\n", + "plt.tick_params(direction='in')\n", + "\n", + "plt.xlim(eigenstatus['xlist'].min(),eigenstatus['xlist'].max())\n", + "\n", + "plt.ylabel('E - E$_{min}$ (eV)',fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.xticks(eigenstatus['high_sym_kpoints'], eigenstatus['labels'], fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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