Merge pull request #265 from DaehoHan/ldr_rev

Add more analyses in the LDR dynamics

Author: alexvakimov

src/libra_py/dynamics/ldr_torch/compute.py

@@ -41,17 +41,19 @@ def __init__(self, params):
         self.prefix = params.get("prefix", "ldr-solution")
         self.device = params.get("device", torch.device("cuda" if torch.cuda.is_available() else "cpu"))
         self.hbar = 1.0
-        self.Hamiltonian_scheme = "symmetrized"
+        self.hamiltonian_scheme = "symmetrized"
         self.q0 = torch.tensor(params.get("q0", [0.0]), dtype=torch.float64, device=self.device)
         self.p0 = torch.tensor(params.get("p0", [0.0]), dtype=torch.float64, device=self.device)
         self.k = torch.tensor(params.get("k", [0.001]), dtype=torch.float64, device=self.device)
         self.mass = torch.tensor(params.get("mass", [2000.0]), dtype=torch.float64, device=self.device)
         self.alpha = torch.tensor(params.get("alpha", [18.0]), dtype=torch.float64, device=self.device)
         self.qgrid = torch.tensor(params.get("qgrid", [[-10 + i * 0.1] for i in range(int((10 - (-10)) / 0.1) + 1)] ), dtype=torch.float64, device=self.device) #(N, D)
         self.ngrids = len(self.qgrid) # N
+        self.ndof = self.qgrid.shape[1] 
         self.nstates = params.get("nstates", 2)
         self.istate = params.get("istate", 0)
-        
+        self.elec_ampl = params.get("elec_ampl", torch.tensor([1.0+0.j]*self.ngrids, dtype=torch.cdouble))
+
         self.save_every_n_steps = params.get("save_every_n_steps", 1)
         self.properties_to_save = params.get("properties_to_save", ["time", "population_right"])
         self.dt = params.get("dt", 0.01)
@@ -60,99 +62,103 @@ def __init__(self, params):
 
         self.E = params.get("E", torch.zeros(self.nstates, self.ngrids, device=self.device) )
 
-        Selec_default = torch.zeros(self.ndim, self.ndim, dtype=torch.cdouble, device=self.device)
+        s_elec_default = torch.zeros(self.ndim, self.ndim, dtype=torch.cdouble, device=self.device)
         for i in range(self.nstates):
             start, end = i * self.ngrids, (i + 1) * self.ngrids
-            Selec_default[start:end, start:end] = torch.eye(self.ngrids, device=self.device)            
-        self.Selec = params.get("Selec", Selec_default )   
+            s_elec_default[start:end, start:end] = torch.eye(self.ngrids, device=self.device)            
+        self.s_elec = params.get("s_elec", s_elec_default )   
 
         # Computed with LDR methods
         self.C0 = torch.zeros(self.ndim, dtype=torch.cdouble, device=self.device)
-        self.Ccurr = torch.zeros(self.ndim, dtype=torch.cdouble, device=self.device)
+        self.C_curr = torch.zeros(self.ndim, dtype=torch.cdouble, device=self.device)
 
-        self.Snucl = torch.eye(self.ngrids, dtype=torch.cdouble, device=self.device)
-        self.Tnucl = torch.zeros(self.ngrids, self.ngrids, dtype=torch.cdouble, device=self.device)
+        self.s_nucl = torch.eye(self.ngrids, dtype=torch.cdouble, device=self.device)
+        self.t_nucl = torch.zeros(self.ngrids, self.ngrids, dtype=torch.cdouble, device=self.device)
 
         self.S, self.H = torch.zeros(self.ndim, self.ndim, dtype=torch.cdouble, device=self.device), torch.zeros(self.ndim, self.ndim, dtype=torch.cdouble, device=self.device)
         self.U = torch.zeros(self.ndim, self.ndim, dtype=torch.cdouble, device=self.device)
+        self.S_half = torch.zeros(self.ndim, self.ndim, dtype=torch.cdouble, device=self.device)
 
         self.time = []
         self.kinetic_energy = []
         self.potential_energy = []
         self.total_energy = []
+        self.average_pos = []
         self.population_right = []
+        self.denmat = []
         self.norm = []
         self.C_save = []
 
     def chi_overlap(self):
         """
-        Compute nuclear overlap matrix Snucl[i, j] for the mesh qmesh.
+        Compute nuclear overlap matrix s_nucl[i, j] for the mesh qmesh 
+        from the Gaussian basis, g(x; q) = \exp(-\alpha * (x-q)**2).
         """
         delta = self.qgrid[:, None, :] - self.qgrid[None, :, :]    # (N, N, D)
         exponent = -0.5 * torch.sum(self.alpha * delta**2, dim=2)  # (N, N)
-        self.Snucl = torch.exp(exponent)
+        self.s_nucl = torch.exp(exponent)
 
     def chi_kinetic(self):
-        r"""
-        Compute nuclear kinetic energy matrix Tnucl[i,j] = <g(x; qgrid[i]) | T | g(x; qgrid[j])>,
-        with T = Σ_ν -½ m_ν^{-1} ∂²/∂x_ν².
+        """
+        Compute nuclear kinetic energy matrix t_nucl[i,j] = <g(x; qgrid[i]) | T | g(x; qgrid[j])>,
+        with T = \sum_{\nu} -0.5* m_ν^{-1} \partial^{2}/\partial x_{\nu}^2.
         """
         delta = self.qgrid[:, None, :] - self.qgrid[None, :, :]               # (N, N, D)
         tau = self.alpha / (2.0 * self.mass) * (1.0 - self.alpha * delta**2)  # (N, N, D)
         tau_sum = torch.sum(tau, dim=2)                                       # (N, N)
 
-        self.Tnucl = self.Snucl * tau_sum                                     # (N, N)
-    
+        self.t_nucl = self.s_nucl * tau_sum                                   # (N, N)
+
     def build_compound_overlap(self):
         """
         Build the compound nuclear-electronic overlap matrix self.S (ndim, ndim)
         """
         N, s, ndim = self.ngrids, self.nstates, self.ndim
 
-        # Reshape Selec[a, b] -> (i, n, j, m) with:
+        # Reshape s_elec[a, b] -> (i, n, j, m) with:
         #   a = i * N + n
         #   b = j * N + m
-        Selec4D = self.Selec.view(s, N, s, N) # (i, n, j, m)
+        s_elec_4d = self.s_elec.view(s, N, s, N) # (i, n, j, m)
 
-        Snucl4D = self.Snucl.unsqueeze(0).unsqueeze(2) # (1, n, 1, m)
+        s_nucl_4d = self.s_nucl[None, :, None, :] # (1, n, 1, m)
 
-        S4D = Selec4D * Snucl4D
+        S_4d = s_elec_4d * s_nucl_4d
 
         # Reshape back to (ndim, ndim) with compound indices
-        self.S = S4D.permute(0, 1, 2, 3).reshape(ndim, ndim)
+        self.S = S_4d.reshape(ndim, ndim)
 
     def build_compound_hamiltonian(self):
         """
         Build the compound nuclear-electronic Hamiltonian self.H (ndim, ndim) using different schemes.
         """
         N, s, ndim = self.ngrids, self.nstates, self.ndim
-        scheme = self.Hamiltonian_scheme
-        Selec4D = self.Selec.view(s, N, s, N)       # (s, N, s, N)
-        T4D = self.Tnucl.unsqueeze(0).unsqueeze(2)  # (1, N, 1, N)
-        S4D = self.Snucl.unsqueeze(0).unsqueeze(2)  # (1, N, 1, N)
+        scheme = self.hamiltonian_scheme
+        s_elec_4d = self.s_elec.view(s, N, s, N)      # (s, N, s, N)
+        T_4d = self.t_nucl[None, :, None, :]          # (1, N, 1, N)
+        S_4d = self.s_nucl[None, :, None, :]          # (1, N, 1, N)
 
         if scheme == 'as_is': # For showing the original non-Hermitian form, not intended to use
-            Ej4D = self.E[None, None, :, :]           # (1, 1, s, N)
-            bracket4D = T4D + Ej4D * S4D
+            E_j_4d = self.E[None, None, :, :]   # (1, 1, s, N)
+            bracket_4d = T_4d + E_j_4d * S_4d
         elif scheme == 'symmetrized':
-            Ei4D = self.E[:, :, None, None]   # (s, N, 1, 1)
-            Ej4D = self.E[None, None, :, :]   # (1, 1, s, N)
-            Eavg4D = 0.5 * (Ei4D + Ej4D)      # (s, N, s, N)
-            bracket4D = T4D + Eavg4D * S4D
+            E_i_4d = self.E[:, :, None, None]   # (s, N, 1, 1)
+            E_j_4d = self.E[None, None, :, :]   # (1, 1, s, N)
+            E_avg_4d = 0.5 * (E_i_4d + E_j_4d)  # (s, N, s, N)
+            bracket_4d = T_4d + E_avg_4d * S_4d
         elif scheme == 'diagonal':
             # Build Kronecker deltas for electronic and nuclear indices
-            delta_ij = torch.eye(s, device=self.device).unsqueeze(1).unsqueeze(3)  # (s, 1, s, 1)
-            delta_nm = torch.eye(N, device=self.device).unsqueeze(0).unsqueeze(2)  # (1, N, 1, N)
-            delta4D = delta_ij * delta_nm
+            delta_ij = torch.eye(s, device=self.device)[:, None, :, None]  # (s, 1, s, 1)
+            delta_nm = torch.eye(N, device=self.device)[None, :, None, :]  # (1, N, 1, N)
+            delta_4d = delta_ij * delta_nm
 
-            Ej4D = self.E[None, None, :, :] # (1, 1, s, N)
-            bracket4D = T4D + Ej4D * S4D * delta4D
+            E_j_4d = self.E[None, None, :, :] # (1, 1, s, N)
+            bracket_4d = T_4d + E_j_4d * S_4d * delta_4d
 
         else:
             raise ValueError(f"Unknown Hamiltonian scheme: {scheme}")
 
-        H4D = Selec4D * bracket4D
-        self.H = H4D.reshape(ndim, ndim)
+        H_4d = s_elec_4d * bracket_4d
+        self.H = H_4d.reshape(ndim, ndim)
 
     def compute_propagator(self):
         """
@@ -166,7 +172,7 @@ def compute_propagator(self):
 
         evals_S, evecs_S = torch.linalg.eigh(S)
 
-        S_half = (evecs_S @ torch.diag(evals_S.sqrt().to(dtype=torch.cdouble)) @ evecs_S.T).to(dtype=torch.cdouble)
+        self.S_half = (evecs_S @ torch.diag(evals_S.sqrt().to(dtype=torch.cdouble)) @ evecs_S.T).to(dtype=torch.cdouble)
         S_invhalf = (evecs_S @ torch.diag((1.0 / evals_S).sqrt().to(dtype=torch.cdouble)) @ evecs_S.T).to(dtype=torch.cdouble)
 
         H_ortho = S_invhalf @ H @ S_invhalf
@@ -176,7 +182,7 @@ def compute_propagator(self):
         exp_diag = torch.diag(torch.exp(-1j * evals_H * dt))
         U_ortho = evecs_H @ exp_diag @ evecs_H.conj().T
 
-        self.U = S_invhalf @ U_ortho @ S_half
+        self.U = S_invhalf @ U_ortho @ self.S_half
 
 
     def initialize_C(self):
@@ -217,7 +223,7 @@ def initialize_C(self):
             delta_eta = -0.5 * torch.dot(xi0 + p0, q0) + 0.5 * torch.dot(xig, qgrid[n]).conj()
             exponent = -1.j * 0.5 * torch.dot(delta_xi, torch.matmul(delta_A_inv, delta_xi)) + 1.j * delta_eta
 
-            self.C0[index] = torch.exp(exponent)
+            self.C0[index] = self.elec_ampl[n] * torch.exp(exponent)
 
         # Normalize
         overlap = torch.matmul(self.S, self.C0)
@@ -230,14 +236,14 @@ def propagate(self):
         Propagate coefficient.
         """
         # Initialize first step with normalized initial wavefunction
-        self.Ccurr = self.C0.clone()
+        self.C_curr = self.C0.clone()
 
         print(F"step = 0")
         self.save_results(0)
 
         for step in range(1, self.nsteps):
-            Cvec = self.Ccurr.clone()
-            self.Ccurr = self.U @ Cvec
+            C_vec = self.C_curr.clone()
+            self.C_curr = self.U @ C_vec
 
             if step % self.save_every_n_steps == 0:
                 print(F"step = {step}")
@@ -247,53 +253,72 @@ def save_results(self, step):
         if "time" in self.properties_to_save:
             self.time.append(step*self.dt)
         if "norm" in self.properties_to_save:
-            overlap = torch.matmul(self.S, self.Ccurr)
-            self.norm.append(torch.sqrt(torch.vdot(self.Ccurr, overlap)))
+            overlap = torch.matmul(self.S, self.C_curr)
+            self.norm.append(torch.sqrt(torch.vdot(self.C_curr, overlap)))
         if "population_right" in self.properties_to_save:
             self.population_right.append(self.compute_populations())
+        if "denmat" in self.properties_to_save:
+            self.denmat.append(self.compute_denmat())
         if "kinetic_energy" in self.properties_to_save:
             self.kinetic_energy.append(self.compute_kinetic_energy())
         if "potential_energy" in self.properties_to_save:
             self.potential_energy.append(self.compute_potential_energy())
         if "total_energy" in self.properties_to_save:
             self.total_energy.append(self.compute_total_energy())
+        if "average_pos" in self.properties_to_save:
+            self.average_pos.append(self.compute_average_pos())
         if "C_save" in self.properties_to_save:
-            self.C_save.append(self.Ccurr)
+            self.C_save.append(self.C_curr)
 
     def compute_populations(self):
         """
         Compute electronic state population for a single step.
         """
         N, s = self.ngrids, self.nstates
-        Cvec = self.Ccurr
+        C_vec = self.C_curr
 
         # Compute SC once: shape (ndim,)
-        SC = self.S @ Cvec
+        SC = self.S @ C_vec
 
-        C_blocks = Cvec.view(s, N)
+        C_blocks = C_vec.view(s, N)
         SC_blocks = SC.view(s, N)
 
         # Compute P[i] = sum_j <C_j|S_{ji}|C_i> = Re[ sum_N (C_j*) * SC_j ]
         P = torch.sum(C_blocks.conj() * SC_blocks, dim=1).real
 
         return P
 
+    def compute_denmat(self):
+        """
+        Compute electronic density matrix for a single step using the orthogonalization.
+        """
+        N, s = self.ngrids, self.nstates
+        C_vec = self.C_curr
+    
+        # Orthogonalize coefficients: C_ortho = S^{1/2} C
+        C_ortho = self.S_half @ C_vec
+      
+        C_blocks = C_ortho.view(s, N)
+    
+        rho = C_blocks @ C_blocks.conj().T # (s, s)
+    
+        return rho
+
     def compute_kinetic_energy(self):
         """
         Compute nuclear kinetic energy as C^+ T C / C^+ S C for a single step.
         """
         N, s, ndim = self.ngrids, self.nstates, self.ndim
 
-        # Rebuild compound kinetic matrix: T4D * Selec4D
-        Selec4D = self.Selec.view(s, N, s, N)
-        T4D = self.Tnucl.unsqueeze(0).unsqueeze(2)  # (1, n, 1, m)
-        T4D_compound = Selec4D * T4D
-        T_compound = T4D_compound.permute(0, 1, 2, 3).reshape(ndim, ndim)
+        # Rebuild compound kinetic matrix: T_4d * s_elec_4d
+        s_elec_4d = self.s_elec.view(s, N, s, N)
+        T_4d = self.t_nucl[None, :, None, :]
+        T_compound = (s_elec_4d * T_4d).reshape(ndim, ndim)
 
-        Cvec = self.Ccurr
+        C_vec = self.C_curr
 
-        numer = torch.vdot(Cvec, T_compound @ Cvec).real
-        denom = torch.vdot(Cvec, self.S @ Cvec).real
+        numer = torch.vdot(C_vec, T_compound @ C_vec).real
+        denom = torch.vdot(C_vec, self.S @ C_vec).real
 
         return numer / denom
 
@@ -304,17 +329,16 @@ def compute_potential_energy(self):
         """
         N, s, ndim = self.ngrids, self.nstates, self.ndim
 
-        Selec4D = self.Selec.view(s, N, s, N)
-        S4D = self.Snucl.unsqueeze(0).unsqueeze(2)  # (1, n, 1, m)
-        Ej4D = self.E[None, None, :, :]  # (1,1,j,m)
+        s_elec_4d = self.s_elec.view(s, N, s, N)
+        S_4d = self.s_nucl[None, :, None, :]
+        E_j_4d = self.E[None, None, :, :]  # (1,1,j,m)
 
-        V4D_compound = Selec4D * (Ej4D * S4D)
-        V_compound = V4D_compound.permute(0, 1, 2, 3).reshape(ndim, ndim)
+        V_compound = (s_elec_4d * (E_j_4d * S_4d)).reshape(ndim, ndim)
 
-        Cvec = self.Ccurr
+        C_vec = self.C_curr
 
-        numer = torch.vdot(Cvec, V_compound @ Cvec).real
-        denom = torch.vdot(Cvec, self.S @ Cvec).real
+        numer = torch.vdot(C_vec, V_compound @ C_vec).real
+        denom = torch.vdot(C_vec, self.S @ C_vec).real
 
         return numer / denom
 
@@ -323,13 +347,37 @@ def compute_total_energy(self):
         """
         Compute total energy as C^+ H C / C^+ S C for a single step.
         """
-        Cvec = self.Ccurr
+        C_vec = self.C_curr
 
-        numer = torch.vdot(Cvec, self.H @ Cvec).real
-        denom = torch.vdot(Cvec, self.S @ Cvec).real
+        numer = torch.vdot(C_vec, self.H @ C_vec).real
+        denom = torch.vdot(C_vec, self.S @ C_vec).real
 
         return numer / denom
+
+    def compute_average_pos(self):
+        """
+        Compute average position as <q_i> = \sum_i C^+ Q C / C^+ S C for a single step.
+        """
+        N, s, ndim = self.ngrids, self.nstates, self.ndim
 
+        C_vec = self.C_curr
+
+        denom = torch.vdot(C_vec, self.S @ C_vec).real
+        s_elec_4d = self.s_elec.view(s, N, s, N)
+        
+        avg_q = []
+        for idof in range(self.ndof):
+            q_med = 0.5 * (self.qgrid[:, None, idof] + self.qgrid[None,:,idof])
+            q_nucl = self.s_nucl * q_med 
+            Q_4d = q_nucl[None, :, None, :]
+            Q_4d_compound = s_elec_4d * Q_4d
+            Q_compound = Q_4d_compound.reshape(ndim, ndim)
+
+            numer = torch.vdot(C_vec, Q_compound @ C_vec).real
+            avg_q.append(numer / denom)
+
+        return avg_q
+
     def save(self):
         torch.save( {"q0":self.q0,
                      "p0":self.p0,
@@ -339,22 +387,24 @@ def save(self):
                      "qgrid":self.qgrid,
                      "nstates":self.nstates,
                      "istate":self.istate,
-                     "Snucl":self.Snucl,
-                     "Tnucl":self.Tnucl,
+                     "s_nucl":self.s_nucl,
+                     "t_nucl":self.t_nucl,
                      "E":self.E,
-                     "Selec":self.Selec,
+                     "s_elec":self.s_elec,
                      "S":self.S,
                      "H":self.H,
                      "U":self.U,
                      "C_save":self.C_save,
                      "save_every_n_steps":self.save_every_n_steps,
-                     "Hamiltonian_scheme": self.Hamiltonian_scheme,
+                     "hamiltonian_scheme": self.hamiltonian_scheme,
                      "dt":self.dt, "nsteps":self.nsteps,
                      "time":self.time,
                      "kinetic_energy":self.kinetic_energy,
                      "potential_energy":self.potential_energy,
                      "total_energy":self.total_energy,
+                     "average_pos":self.average_pos,
                      "population_right":self.population_right,
+                     "denmat":self.denmat,
                      "norm":self.norm
                     }, F"{self.prefix}.pt" )