Poisson-dev and support for BTD and Overlap Matrix in NEGF #105
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…o localize the lead hamiltonian
…f energy algorithm
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| class Bloch(object): | ||
| def __init__(self,bloch_factor) -> None: |
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是用Bloch定理展开k点的倍数，比如可以设为[3,3,1]，则将一个k点展开为了9个。具体见新增的docstring
| self.hd, self.sd, _, _, _, _ = self.hamiltonian.get_hs_device(kpoint, self.V, block_tridiagonal) | ||
| self.kpoint = torch.tensor(kpoint) | ||
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| self.oldV = None |
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这块判断old_V 的逻辑其实有点冗余，初始化的时候设置一个self.V，然后每次和输入的V做对比看看要不要更新就行了
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| if not self.useBloch: | ||
| if not hasattr(self, "HL") or abs(self.voltage_old-self.voltage)>1e-6 or max(abs(self.kpoint-torch.tensor(kpoint)))>1e-6: |
| @@ -45,6 +45,8 @@ def __init__( | |||
| self.node_field = node_field | |||
| self.out_field = out_field | |||
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| self.atom_norbs = [] | |||
| stru_options:dict, | ||
| unit: str, | ||
| results_path:Optional[str]=None, | ||
| torch_device: Union[str, torch.device]=torch.device('cpu') |
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那这个字段会和NEGF器件区域的device相互冲突的。这里是有意改为torch_device。
| edge_field=AtomicDataDict.EDGE_FEATURES_KEY, | ||
| node_field=AtomicDataDict.NODE_FEATURES_KEY, | ||
| out_field=AtomicDataDict.HAMILTONIAN_KEY, | ||
| dtype= model.dtype, |
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好奇如果这边model是32的，生成出来的哈密顿量是不是后面还要align一下dtype
| alldata = AtomicData.to_AtomicDataDict(alldata.to(self.torch_device)) | ||
| self.alldata = self.model.idp(alldata) | ||
| self.alldata[AtomicDataDict.KPOINT_KEY] = \ | ||
| torch.nested.as_nested_tensor([torch.as_tensor(HS_device["kpoints"], dtype=self.model.dtype, device=self.torch_device)]) |
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| structure_leads = {} | ||
| structure_device = self.structase[self.device_id[0]:self.device_id[1]] | ||
| structure_device.pbc = self.pbc_negf |
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| l_start = int(np.sum(self.h2k.atom_norbs[:lead_id[0]])) |
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这个用OrbitalMapper里的atom_norbs替换掉吧
| subblocks = [0]+subblocks | ||
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| for ik in range(HK.shape[0]): |
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后面的时候或许需要把k axis cat到一块，方便RGF直接vmap GPU并行，现在可以不管
| counted = 0 | ||
| for i in range(len(hd_k)): # TODO: this part may have probelms when V!=0 | ||
| l_slice = slice(counted, counted+hd_k[i].shape[0]) | ||
| hd_k[i] = hd_k[i] - V[l_slice,l_slice]@sd_k[i] |
| @@ -251,8 +624,262 @@ def device_norbs(self): | |||
| """ | |||
| return the number of atoms in the device Hamiltonian | |||
| """ | |||
| return self.atom_norbs[self.device_id[0]:self.device_id[1]] | |||
| return self.h2k.atom_norbs[self.device_id[0]:self.device_id[1]] | |||
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| class Grid(object): | ||
| # define the grid in 3D space | ||
| def __init__(self,xg,yg,zg,xa,ya,za): |
| for q in range(num_of_matrices - 2, -1, -1): # Recursive algorithm | ||
| grl[q] = grd[q + 1] @ mat_l_list[q] @ gr_left[q] # (B5) We get the off-diagonal blocks for free. | ||
| gru[q] = gr_left[q] @ mat_u_list[q] @ grd[q + 1] # (B6) because we need .Tthem.T for the next calc: | ||
| grd[q] = gr_left[q] + gr_left[q] @ mat_u_list[q] @ grl[q] # (B4) I suppose I could also use the lower. | ||
| g_trans = gr_left[q] @ mat_u_list[q] @ g_trans | ||
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last column of Gr. Used for spectral function corresponding to the right electrode A_R in block-tridiagonal case.
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try to merge poisson-dev to main