def test_dynamics(agg_list, energies): # Adding the energies to the molecules. Neeed to be done before agg with qr.energy_units("1/cm"): for i, mol in enumerate(agg_list): mol.set_energy(1, energies[i]) # Creation of the aggregate for dynamics. multiplicity can be 1 agg = qr.Aggregate(molecules=agg_list) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build(mult=1) agg.diagonalize() # Creating a propagation axis length t13_ax plus padding with intervals 1 t2_prop_axis = qr.TimeAxis(0.0, 1000, 1) # Generates the propagator to describe motion in the aggregate prop_Redfield = agg.get_ReducedDensityMatrixPropagator( t2_prop_axis, relaxation_theory="stR", time_dependent=False, secular_relaxation=True ) # Obtaining the density matrix shp = agg.get_Hamiltonian().dim rho_i1 = qr.ReducedDensityMatrix(dim=shp, name="Initial DM") # Setting initial conditions rho_i1.data[shp-1,shp-1] = 1.0 # Propagating the system along the t13_ax_ax time axis rho_t1 = prop_Redfield.propagate(rho_i1, name="Redfield evo from agg") rho_t1.plot(coherences=False, axis=[0,t2_prop_axis.length,0,1.0], show=True)
def calcTwoD(loopNum): ''' Caculates a 2d spectrum for both perpendicular and parael lasers using aceto bands and accurate lineshapes''' container = [] agg = qr.Aggregate(molecules=forAggregate) with qr.energy_units('1/cm'): for i in range(len(forAggregate)): agg.monomers[i].set_energy(1, random.gauss(energy, staticDis)) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build(mult=2) agg.diagonalize() tcalc_para = qr.TwoDResponseCalculator(t1axis=t13, t2axis=t2s, t3axis=t13, system=agg) tcalc_para.bootstrap(rwa, pad=padding, verbose=True, lab=labPara, printEigen=False, printResp='paraResp')#, printResp='paraResp' twods_para = tcalc_para.calculate() paraContainer = twods_para.get_TwoDSpectrumContainer() container.append(paraContainer) tcalc_perp = qr.TwoDResponseCalculator(t1axis=t13, t2axis=t2s, t3axis=t13, system=agg) tcalc_perp.bootstrap(rwa, pad=padding, verbose=True, lab=labPerp, printEigen=False)#, printResp='perpResp' twods_perp = tcalc_perp.calculate() perpContainer = twods_perp.get_TwoDSpectrumContainer() container.append(perpContainer) return container
def calcTwoDMock(loopNum): ''' Calculates a2d spectrum container for all time points on t2 axis for both perpendicular and parallel lasers. Uses quantarhei only and speeds up runtime by using simple lineshapes''' container = [] agg = qr.Aggregate(forAggregateMock) with qr.energy_units('1/cm'): for i in range(len(forAggregateMock)): agg.monomers[i].set_energy(1, random.gauss(energy, staticDis)) agg.set_coupling_by_dipole_dipole() agg.build(mult=2) agg.diagonalize() rT = agg.get_RelaxationTensor(t2s, relaxation_theory='stR') eUt = qr.EvolutionSuperOperator(t2s, rT[1], rT[0]) # rT[1] = Hamiltonian eUt.set_dense_dt(t2Step) eUt.calculate(show_progress=False) mscPara = qr.MockTwoDResponseCalculator(t13, t2s, t13) mscPara.bootstrap(rwa=rwa, shape="Gaussian") contPara = mscPara.calculate_all_system(agg, eUt, labParaMock, show_progress=True) cont2DPara = contPara.get_TwoDSpectrumContainer(stype=qr.signal_TOTL) container.append(cont2DPara) mscPerp = qr.MockTwoDResponseCalculator(t13, t2s, t13) mscPerp.bootstrap(rwa=rwa, shape="Gaussian") contPerp = mscPerp.calculate_all_system(agg, eUt, labPerpMock, show_progress=True) cont2DPerp = contPerp.get_TwoDSpectrumContainer(stype=qr.signal_TOTL) container.append(cont2DPerp) return container
def _temp_build_agg(self, agg_list = None , mult = 1, diagonalize = True): if not agg_list: agg_list = self.mol_list agg = qr.Aggregate(molecules=agg_list) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build(mult=mult) if diagonalize: agg.diagonalize() return agg
def build_agg(self, agg_list = None, mult = 1, diagonalize = True): if not agg_list: agg_list = self.mol_list agg = qr.Aggregate(molecules=agg_list) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build(mult=mult) if diagonalize: agg.diagonalize() self.agg = agg self.num_mol = agg.nmono self.rwa = agg.get_RWA_suggestion()
def main(): with qr.energy_units("1/cm"): mol1 = qr.Molecule([0.0, 12000.0]) mol2 = qr.Molecule([0.0, 12100.0]) mol3 = qr.Molecule([0.0, 12100.0]) agg = qr.Aggregate([mol1, mol2, mol3]) m1 = qr.Mode(100) mol1.add_Mode(m1) m2 = qr.Mode(100) mol2.add_Mode(m2) m3 = qr.Mode(100) mol3.add_Mode(m3) agg.build(mult=1) print(agg.Ntot)
def calcTwoD(n_loopsum): # ''' Caculates a 2d spectrum for both perpendicular and parael lasers using aceto bands and accurate lineshapes''' container = [] #energies0 = [energy - (100 * num_mol / 2) + i * 100 for i in range(num_mol)] #energies0 = [energy] * num_mol # Giving random energies to the moleucles according to a gauss dist with qr.energy_units("1/cm"): for i, mol in enumerate(for_agg): mol.set_energy(1, random.gauss(energy, static_dis)) #mol.set_energy(1, energies0[i]) agg = qr.Aggregate(molecules=for_agg) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build(mult=2) print(np.diagonal(agg.HH[1:num_mol + 1, 1:num_mol + 1])) agg.diagonalize() rwa = agg.get_RWA_suggestion() print(np.diagonal(agg.HH[1:num_mol + 1, 1:num_mol + 1])) # Initialising the twod response calculator for the paralell laser resp_calc_temp = qr.TwoDResponseCalculator(t1axis=t13_ax, t2axis=t2_ax, t3axis=t13_ax, system=agg) # Bootstrap is the place to add 0-padding to the response signal # printEigen=True prints eigenvalues, printResp='string' prints response # Response is calculated Converted into spectrum Stored in a container for lab in labs: resp_calc = copy.deepcopy(resp_calc_temp) resp_calc.bootstrap(rwa, pad=padding, lab=lab) resp_cont = resp_calc.calculate() spec_cont = resp_cont.get_TwoDSpectrumContainer() container.append(spec_cont) return container
def getEigen(): print('nM', nM) agg = qr.Aggregate(molecules=forAggregate) for j in range(nM): with qr.energy_units('1/cm'): agg.monomers[j].set_energy(1, random.gauss(energies[j], staticDis)) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build() #mult=2 N = len(forAggregate) H = agg.get_Hamiltonian() print(H) SS = H.diagonalize() print(H) trans1 = SS[1:N + 1, 1:N + 1] H.undiagonalize() hamil = agg.HH[1:N + 1, 1:N + 1] with open('transData.txt', 'a') as f: f.write('Hamiltonian\n') #np.savetxt(f, agg.HH[1:N+1,1:N+1]) np.savetxt(f, hamil) f.write('Transformation Matrix\n') np.savetxt(f, trans1) agg.diagonalize() diag = agg.HH[1:N + 1, 1:N + 1] with open('transData.txt', 'a') as f: f.write('Diagonalized\n') np.savetxt(f, agg.HH[1:N + 1, 1:N + 1]) f.write('Dipoles\n') np.savetxt(f, agg.D2)
def calcTwoD(n_loopsum): # ''' Caculates a 2d spectrum for both perpendicular and parael lasers using aceto bands and accurate lineshapes''' #energies0 = [energy - (100 * nM / 2) + i * 100 for i in range(nM)] #energies0 = [energy] * nM # Giving random energies to the moleucles according to a gauss dist with qr.energy_units("1/cm"): for i, mol in enumerate(for_agg): mol.set_energy(1, random.gauss(energy, static_dis)) #mol.set_energy(1, energies0[i]) agg = qr.Aggregate(molecules=for_agg) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build(mult=1) HH = agg.get_Hamiltonian() pig_ens = np.diagonal(HH.data[1:nM+1,1:nM+1])\ / (2.0*const.pi*const.c*1.0e-13) en_order = np.argsort(pig_ens) SS = HH.diagonalize() eig_vecs = np.transpose(SS[1:nM + 1, 1:nM + 1]) state_ens = np.diagonal(HH.data[1:nM+1,1:nM+1])\ / (2.0*const.pi*const.c*1.0e-13) agg.diagonalize() dips = agg.D2[0][1:nM + 1] dip_order = np.flip(np.argsort(dips)) state_data = { 'pig_ens': pig_ens, 'en_order': en_order, 'eig_vecs': eig_vecs, 'state_ens': state_ens, 'dips': dips, 'dip_order': dip_order } return state_data
print("* *") print("***********************************************************") ############################################################################### # # Model system definition # ############################################################################### # Three molecules with qr.energy_units("1/cm"): m1 = qr.Molecule([0.0, 10100.0]) m2 = qr.Molecule([0.0, 10300.0]) m3 = qr.Molecule([0.0, 10000.0]) # Aggregate is built from the molecules agg = qr.Aggregate([m1, m2, m3]) # Couplings between them are set with qr.energy_units("1/cm"): agg.set_resonance_coupling(0, 1, 80.0) agg.set_resonance_coupling(0, 2, 100.0) # Interaction with the bath is set through bath correlation functions timea = qr.TimeAxis(0.0, 500, 1.0) cpar1 = dict(ftype="OverdampedBrownian-HighTemperature", reorg=50, cortime=50, T=300) cpar2 = dict(ftype="OverdampedBrownian-HighTemperature", reorg=50, cortime=50,
* J =""", J, "1/cm\n*", """ ******************************************************************************* """) ########################################################################### # # Model system definition # ########################################################################### # Two molecules with qr.energy_units("1/cm"): m1 = qr.Molecule([0.0, E1]) m2 = qr.Molecule([0.0, E2]) # Aggregate is built from the molecules agg = qr.Aggregate([m1, m2]) # Couplings between them are set with qr.energy_units("1/cm"): agg.set_resonance_coupling(0, 1, J) # Interaction with the bath is set through bath correlation functions timea = qr.TimeAxis(0.0, N, dt) cpar1 = dict(ftype="OverdampedBrownian-HighTemperature", reorg=lamb, cortime=tc, T=Temperature) #cpar2 = dict(ftype="OverdampedBrownian-HighTemperature", reorg=50, # cortime=50, T=300) with qr.energy_units("1/cm"):
# -*- coding: utf-8 -*- #<remove> _show_plots_ = False #</remove> import quantarhei as qr en = [0.0, 1.0] m1 = qr.Molecule(name="Mol1", elenergies=en) m2 = qr.Molecule(name="Mol2", elenergies=en) ag = qr.Aggregate(name="Homodimer") ag.add_Molecule(m1) ag.add_Molecule(m2) ag.set_resonance_coupling(0, 1, 0.1) ag.build(mult=1) H = ag.get_Hamiltonian() #with qr.energy_units("1/cm"): # print(H) # # Here we test generation of states with 3 level molecules # en = [0.0, 10100.0] #, 20200.0] with qr.energy_units("1/cm"):
_show_plots_ = False import numpy import quantarhei as qr qr.Manager().gen_conf.legacy_relaxation = True print("Preparing a model system:") with qr.energy_units("1/cm"): mol1 = qr.Molecule([0.0, 12010]) mol2 = qr.Molecule([0.0, 12000]) mol3 = qr.Molecule([0.0, 12100]) mol4 = qr.Molecule([0.0, 12110]) agg = qr.Aggregate([mol1, mol2, mol3, mol4]) agg.set_resonance_coupling(2, 3, qr.convert(100.0, "1/cm", "int")) agg.set_resonance_coupling(1, 3, qr.convert(100.0, "1/cm", "int")) agg.set_resonance_coupling(1, 2, qr.convert(0.0, "1/cm", "int")) qr.save_parcel(agg, "agg.qrp") agg2 = qr.load_parcel("agg.qrp") agg2.build() H = agg2.get_Hamiltonian() print("...done") print("Setting up Lindblad form relaxation:") Ndim = 5 with qr.eigenbasis_of(H):
####################################################################### # Getting aggregate data ####################################################################### file_name = 'data/eigen_data.txt' f = open(file_name, "w+") f.close() for i in range(n_per_loop): for_agg_copy = for_agg with qr.energy_units("1/cm"): for i, mol in enumerate(for_agg_copy): mol.set_energy(1, random.gauss(energy, static_dis)) agg = qr.Aggregate(molecules=for_agg_copy) agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build() save_eigen_data(agg=agg, file=file_name) pig_en, state_en, eig_vecs, state_dips, dip_order, en_order = extracting_eigen_data( file_name) ####################################################################### # Analysis ####################################################################### print('dipoles') print(state_dips[0]) print('order')
matsubara=20) with qr.energy_units("1/cm"): cfce1 = qr.CorrelationFunction(ta, cfce_params1) cfce2 = qr.CorrelationFunction(ta, cfce_params2) m1 = qr.Molecule("M1", [0.0, 12100]) m1.set_dipole(0, 1, [0.0, 3.0, 0.0]) m1.set_transition_environment((0, 1), cfce1) m1.position = [0.0, 0.0, 0.0] m2 = qr.Molecule("M1", [0.0, 12000]) m2.set_dipole(0, 1, [0.0, 1.0, 1.0]) m2.position = [5.0, 0.0, 0.0] m2.set_transition_environment((0, 1), cfce2) # create an aggregate AG = qr.Aggregate("TestAggregate") #AG.set_egcf_matrix(cm) # fill the cluster with monomers AG.add_Molecule(m1) AG.add_Molecule(m2) # setting coupling by dipole-dipole formula AG.set_coupling_by_dipole_dipole() AG.build() HH = AG.get_Hamiltonian() (RR, ham) = AG.get_RelaxationTensor(ta, relaxation_theory="standard_Redfield") a2 = qr.AbsSpect(ta, AG, relaxation_tensor=RR)
def run(omega, HR, dE, JJ, rate, E0, vib_loc="up", use_vib=True, stype=qr.signal_REPH, make_movie=False, save_eUt=False, t2_save_pathways=[], dname=None, trimer=None, disE=None): """Runs a complete set of simulations for a single set of parameters If disE is not None it tries to run averaging over Gaussian energetic disorder. """ if dname is None: dname = "sim_" + vib_loc use_trimer = trimer["useit"] rate_sp = trimer["rate"] # # PARAMETERS FROM INPUT FILE # dip1 = INP.dip1 # [1.5, 0.0, 0.0] dip2 = INP.dip2 # [-1.0, -1.0, 0.0] width = INP.feature_width # 100.0 width2 = INP.feature_width2 normalize_maps_to_maximu = False trim_maps = False units = "1/cm" with qr.energy_units(units): data_descr = "_dO=" + str(dE) + "_omega=" + str(omega) + "_HR=" + str( HR) + "_J=" + str(JJ) if use_vib: sys_char = "_vib" else: sys_char = "_ele" data_ext = sys_char + ".png" obj_ext = sys_char + ".qrp" # parameters of the SP if use_trimer: E2 = trimer["E2"] epsa = (E0 + E2) / 2.0 DE = trimer["DE"] J2 = 0.5 * numpy.sqrt(((E0 - E2)**2) - (DE**2)) ESP2 = epsa + DE / 2.0 ESP1 = epsa - DE / 2.0 # # Model system is a dimer of molecules # with qr.energy_units("1/cm"): if not use_trimer: mol1 = qr.Molecule([0.0, E0]) mol2 = qr.Molecule([0.0, E0 + dE]) print("Monomer 1 energy:", E0) print("Monomer 2 energy:", E0 + dE) else: if disE is not None: mol1 = qr.Molecule([0.0, ESP2 + disE[0]]) mol2 = qr.Molecule([0.0, E0 + dE + disE[1]]) print("Monomer 1 (SP_high) energy:", ESP2 + disE[0]) print("Monomer 2 (B) energy:", E0 + dE + disE[1]) else: mol1 = qr.Molecule([0.0, ESP2]) mol2 = qr.Molecule([0.0, E0 + dE]) print("Monomer 1 (SP_high) energy:", ESP2) print("Monomer 2 (B) energy:", E0 + dE) if disE is not None: mol3 = qr.Molecule([0.0, ESP1 + disE[2]]) print("Monomer 3 (SP_low) energy:", ESP1 + disE[2]) else: mol3 = qr.Molecule([0.0, ESP1]) print("Monomer 3 (SP_low) energy:", ESP1) mol3.set_transition_width((0, 1), width2) mol3.set_dipole(0, 1, trimer["dipsp"]) mol1.set_transition_width((0, 1), width2) mol1.set_dipole(0, 1, dip1) mol2.set_transition_width((0, 1), width) mol2.set_dipole(0, 1, dip2) if use_trimer: agg = qr.Aggregate([mol1, mol2, mol3]) else: agg = qr.Aggregate([mol1, mol2]) if use_trimer: with qr.energy_units("1/cm"): agg.set_resonance_coupling(0, 1, JJ) print("B - SP_high coupling:", JJ) agg.set_resonance_coupling(0, 2, J2) print("SP coupling:", J2) else: with qr.energy_units("1/cm"): agg.set_resonance_coupling(0, 1, JJ) # # Electronic only aggregate # agg_el = agg.deepcopy() # # if nuclear vibrations are to be added, do it here # if use_vib: with qr.energy_units("1/cm"): mod1 = qr.Mode(omega) mod2 = qr.Mode(omega) if vib_loc == "down": set_vib = [True, False] elif vib_loc == "up": set_vib = [False, True] elif vib_loc == "both": set_vib = [True, True] else: raise Exception("Unknown location of the vibrations") if set_vib[0]: print("Vibrations set for SP_high molecule") mol1.add_Mode(mod1) mod1.set_nmax(0, INP.no_g_vib) mod1.set_nmax(1, INP.no_e_vib) mod1.set_HR(1, HR) if set_vib[1]: print("Vibrations set for B molecule") mol2.add_Mode(mod2) mod2.set_nmax(0, INP.no_g_vib) mod2.set_nmax(1, INP.no_e_vib) mod2.set_HR(1, HR) agg3 = agg.deepcopy() agg.build(mult=1) agg_el.build(mult=1) HH = agg.get_Hamiltonian() He = agg_el.get_Hamiltonian() # # Laboratory setup # lab = qr.LabSetup() lab.set_polarizations(pulse_polarizations=[X, X, X], detection_polarization=X) t2_N_steps = INP.t2_N_steps t2_time_step = INP.t2_time_step time2 = qr.TimeAxis(0.0, t2_N_steps, t2_time_step) cont_p = qr.TwoDResponseContainer(t2axis=time2) cont_m = qr.TwoDResponseContainer(t2axis=time2) # # spectra will be indexed by the times in the time axis `time2` # cont_p.use_indexing_type(time2) # # We define two-time axes, which will be FFTed and will define # the omega_1 and omega_3 axes of the 2D spectrum # t1_N_steps = INP.t1_N_steps t1_time_step = INP.t1_time_step t3_N_steps = INP.t3_N_steps t3_time_step = INP.t3_time_step t1axis = qr.TimeAxis(0.0, t1_N_steps, t1_time_step) t3axis = qr.TimeAxis(0.0, t3_N_steps, t3_time_step) # # This calculator calculated 2D spectra from the effective width # msc = qr.MockTwoDResponseCalculator(t1axis, time2, t3axis) with qr.energy_units("1/cm"): msc.bootstrap(rwa=E0, shape="Gaussian") # # System-bath interaction including vibrational states # operators = [] rates = [] if use_trimer: print("Relaxation rates: ", rate, rate_sp) with qr.eigenbasis_of(He): if He.data[3, 3] < He.data[2, 2]: Exception("Electronic states not orderred!") operators.append(qr.qm.ProjectionOperator(2, 3, dim=He.dim)) with qr.energy_units("1/cm"): print("2<-3", He.data[2, 2], He.data[3, 3]) rates.append(rate) print("Transfer time B -> SP:", 1.0 / rate) if He.data[2, 2] < He.data[1, 1]: Exception("Electronic states not orderred!") operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim)) with qr.energy_units("1/cm"): print("1<-2", He.data[1, 1], He.data[2, 2]) rates.append(rate_sp) print("Transfer time P+ -> P-:", 1.0 / rate_sp) # include detailed balace if detailed_balance: with qr.eigenbasis_of(He): T = INP.temperature #77.0 Den = (He.data[3, 3] - He.data[2, 2]) / (kB_int * T) operators.append(qr.qm.ProjectionOperator(3, 2, dim=He.dim)) thermal_fac = numpy.exp(-Den) rates.append(rate * thermal_fac) else: with qr.eigenbasis_of(He): if He.data[2, 2] < He.data[1, 1]: Exception("Electronic states not orderred!") operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim)) rates.append(rate) # include detailed balace if detailed_balance: with qr.eigenbasis_of(He): T = INP.temperature #77.0 Den = (He.data[2, 2] - He.data[1, 1]) / (kB_int * T) operators.append(qr.qm.ProjectionOperator(2, 1, dim=He.dim)) thermal_fac = numpy.exp(-Den) rates.append(rate * thermal_fac) sbi = qr.qm.SystemBathInteraction(sys_operators=operators, rates=rates) sbi.set_system(agg) # # Liouville form for relaxation # LF = qr.qm.ElectronicLindbladForm(HH, sbi, as_operators=True) # # Pure dephasing # p_deph = qr.qm.ElectronicPureDephasing(agg, dtype="Gaussian") # we simplify calculations by converting dephasing to # corresponding Lorentzian form p_deph.convert_to("Lorentzian") eUt = qr.qm.EvolutionSuperOperator(time2, HH, relt=LF, pdeph=p_deph, mode="all") eUt.set_dense_dt(INP.fine_splitting) # # We calculate evolution superoperator # eUt.calculate(show_progress=False) # save the evolution operator if save_eUt: eut_name = os.path.join( dname, "eUt" + "_omega2=" + str(omega) + data_descr + obj_ext) eUt.save(eut_name) # # Prepare aggregate with all states (including 2-EX band) # agg3.build(mult=2) agg3.diagonalize() pways = dict() olow_cm = omega - INP.omega_uncertainty / 2.0 ohigh_cm = omega + INP.omega_uncertainty / 2.0 olow = qr.convert(olow_cm, "1/cm", "int") ohigh = qr.convert(ohigh_cm, "1/cm", "int") for t2 in time2.data: # this could save some memory of pathways become too big pways = dict() print("T2 =", t2) twod = msc.calculate_one_system(t2, agg3, eUt, lab, pways=pways, dtol=1.0e-12, selection=[["omega2", [olow, ohigh]]]) pws = pways[str(t2)] npa = len(pws) print(" p:", npa) has_R = False has_NR = False for pw in pws: if pw.pathway_type == "NR": has_NR = True elif pw.pathway_type == "R": has_R = True print(" R:", has_R, ", NR:", has_NR) if t2 in t2_save_pathways: pws_name = os.path.join( dname, "pws_t2=" + str(t2) + "_omega2=" + str(omega) + data_descr + obj_ext) qr.save_parcel(pways[str(t2)], pws_name) cont_p.set_spectrum(twod) twod = msc.calculate_one_system(t2, agg3, eUt, lab, pways=pways, dtol=1.0e-12, selection=[["omega2", [-ohigh, -olow]]]) pws = pways[str(t2)] npa = len(pws) print(" m:", npa) has_R = False has_NR = False for pw in pws: if pw.pathway_type == "NR": has_NR = True elif pw.pathway_type == "R": has_R = True print(" R:", has_R, ", NR:", has_NR) if t2 in t2_save_pathways: pws_name = os.path.join( dname, "pws_t2=" + str(t2) + "_omega2=" + str(-omega) + data_descr + obj_ext) qr.save_parcel(pways[str(t2)], pws_name) cont_m.set_spectrum(twod) if make_movie: with qr.energy_units("1/cm"): cont_p.make_movie("mov.mp4") # # Save aggregate when a single calculation is done # if save_eUt: fname = os.path.join(dname, "aggregate.qrp") agg3.save(fname) # # Window function for subsequenty FFT # window = func.Tukey(time2, r=INP.tukey_window_r, sym=False) # # FFT with the window function # # Specify REPH, NONR or `total` to get different types of spectra # print("Calculating FFT of the 2D maps") #fcont = cont.fft(window=window, dtype=stype) #, dpart="real", offset=0.0) fcont_p_re = cont_p.fft(window=window, dtype=qr.signal_REPH) fcont_p_nr = cont_p.fft(window=window, dtype=qr.signal_NONR) fcont_p_to = cont_p.fft(window=window, dtype=qr.signal_TOTL) if normalize_maps_to_maximu: fcont_p_re.normalize2(dpart=qr.part_ABS) fcont_p_nr.normalize2(dpart=qr.part_ABS) fcont_p_to.normalize2(dpart=qr.part_ABS) fcont_m_re = cont_m.fft(window=window, dtype=qr.signal_REPH) fcont_m_nr = cont_m.fft(window=window, dtype=qr.signal_NONR) fcont_m_to = cont_m.fft(window=window, dtype=qr.signal_TOTL) if normalize_maps_to_maximu: fcont_m_re.normalize2(dpart=qr.part_ABS) fcont_m_nr.normalize2(dpart=qr.part_ABS) fcont_m_to.normalize2(dpart=qr.part_ABS) if trim_maps: twin = INP.trim_maps_to with qr.energy_units("1/cm"): fcont_p_re.trimall_to(window=twin) fcont_p_nr.trimall_to(window=twin) fcont_p_to.trimall_to(window=twin) show_omega = omega with qr.frequency_units("1/cm"): sp1_p_re, show_Npoint1 = fcont_p_re.get_nearest(show_omega) sp2_p_re, show_Npoint2 = fcont_p_re.get_nearest(-show_omega) sp1_p_nr, show_Npoint1 = fcont_p_nr.get_nearest(show_omega) sp2_p_nr, show_Npoint2 = fcont_p_nr.get_nearest(-show_omega) sp1_p_to, show_Npoint1 = fcont_p_to.get_nearest(show_omega) sp2_p_to, show_Npoint2 = fcont_p_to.get_nearest(-show_omega) sp1_m_re, show_Npoint1 = fcont_m_re.get_nearest(show_omega) sp2_m_re, show_Npoint2 = fcont_m_re.get_nearest(-show_omega) sp1_m_nr, show_Npoint1 = fcont_m_nr.get_nearest(show_omega) sp2_m_nr, show_Npoint2 = fcont_m_nr.get_nearest(-show_omega) sp1_m_to, show_Npoint1 = fcont_m_to.get_nearest(show_omega) sp2_m_to, show_Npoint2 = fcont_m_to.get_nearest(-show_omega) with qr.energy_units(units): if show_plots: # # Spots to look at in detail # with qr.energy_units("1/cm"): with qr.eigenbasis_of(He): Ep_l = He.data[1, 1] Ep_u = He.data[2, 2] Ep = numpy.zeros((4, 2)) Ep[0, 0] = Ep_l Ep[0, 1] = Ep_l Ep[1, 0] = Ep_l Ep[1, 1] = Ep_u Ep[2, 0] = Ep_u Ep[2, 1] = Ep_l Ep[3, 0] = Ep_u Ep[3, 1] = Ep_u print("\nPlotting and saving spectrum at frequency:", fcont_p_re.axis.data[show_Npoint1], units) fftf_1 = os.path.join( dname, "twod_fft" + data_descr + "_stype=REPH" + "_omega=" + str(omega) + data_ext) sp1_p_re.plot(Npos_contours=10, spart=qr.part_ABS, label="Rephasing\n $\omega=" + str(omega) + "$ cm$^{-1}$", text_loc=[0.05, 0.1], show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)], show_diagonal="-k") sp1_p_re.savefig(fftf_1) print("... saved into: ", fftf_1) fftf_2 = os.path.join( dname, "twod_fft" + data_descr + "_stype=NONR" + "_omega=" + str(omega) + data_ext) sp1_p_nr.plot(Npos_contours=10, spart=qr.part_ABS, label="Non-rephasing\n $\omega=" + str(omega) + "$ cm$^{-1}$", text_loc=[0.05, 0.1], show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)], show_diagonal="-k") sp1_p_nr.savefig(fftf_2) print("... saved into: ", fftf_2) fftf_3 = os.path.join( dname, "twod_fft" + data_descr + "_stype=tot" + "_omega=" + str(omega) + data_ext) sp1_p_to.plot(Npos_contours=10, spart=qr.part_ABS, label="Total\n $\omega=" + str(omega) + "$ cm$^{-1}$", text_loc=[0.05, 0.1], show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)], show_diagonal="-k") sp1_p_to.savefig(fftf_3) print("... saved into: ", fftf_3) # # Point evolutions at the expected peak positions # for ii in range(4): points = fcont_p_re.get_point_evolution( Ep[ii, 0], Ep[ii, 1], fcont_p_re.axis) points.apply_to_data(numpy.abs) if ii >= 3: points.plot(show=True) else: points.plot(show=False) print("\nPlotting and saving spectrum at frequency:", fcont_m_re.axis.data[show_Npoint2], units) fftf_4 = os.path.join( dname, "twod_fft" + data_descr + "_stype=REPH" + "_omega=" + str(-omega) + data_ext) sp2_m_re.plot(Npos_contours=10, spart=qr.part_ABS, label="Rephasing\n $\omega=" + str(-omega) + "$ cm$^{-1}$", text_loc=[0.05, 0.1], show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)], show_diagonal="-k") sp2_m_re.savefig(fftf_4) print("... saved into: ", fftf_4) fftf_5 = os.path.join( dname, "twod_fft" + data_descr + "_stype=NONR" + "_omega=" + str(-omega) + data_ext) sp2_m_nr.plot(Npos_contours=10, spart=qr.part_ABS, label="Non-rephasing\n $\omega=" + str(-omega) + "$ cm$^{-1}$", text_loc=[0.05, 0.1], show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)], show_diagonal="-k") sp2_m_nr.savefig(fftf_5) print("... saved into: ", fftf_5) fftf_6 = os.path.join( dname, "twod_fft" + data_descr + "_stype=tot" + "_omega=" + str(-omega) + data_ext) sp2_m_to.plot(Npos_contours=10, spart=qr.part_ABS, label="Total\n $\omega=" + str(-omega) + "$ cm$^{-1}$", text_loc=[0.05, 0.1], show_states=[Ep_l, Ep_u, Ep_u + numpy.abs(omega)], show_diagonal="-k") sp2_m_to.savefig(fftf_6) print("... saved into: ", fftf_6) # # Point evolutions at the expected peak positions # for ii in range(4): points = fcont_p_re.get_point_evolution( Ep[ii, 0], Ep[ii, 1], fcont_m_re.axis) points.apply_to_data(numpy.abs) if ii >= 3: points.plot(show=True) else: points.plot(show=False) save_containers = False if save_containers: fname = os.path.join(dname, "cont_p" + data_descr + obj_ext) print("Saving container into: " + fname) cont_p.save(fname) fname = os.path.join(dname, "cont_m" + data_descr + obj_ext) print("Saving container into: " + fname) cont_m.save(fname) import resource memo = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024 * 1024) print("Memory usage: ", memo, "in MB") return (sp1_p_re, sp1_p_nr, sp2_m_re, sp2_m_nr)
def run(omega, HR, dE, JJ, rate, E0, vib_loc="up", use_vib=True, detailed_balance=False, temperature=77.0, stype=qr.signal_REPH, save_eUt=False, t2_save_pathways=[], dname=None, trimer=None, disE=None): """Runs a complete set of simulations for a single set of parameters If disE is not None it tries to run averaging over Gaussian energetic disorder. """ if dname is None: dname = "sim_" + vib_loc use_trimer = trimer["useit"] rate_sp = trimer["rate"] # # PARAMETERS FROM INPUT FILE # dip1 = INP.dip1 # [1.5, 0.0, 0.0] dip2 = INP.dip2 # [-1.0, -1.0, 0.0] width = INP.feature_width # 100.0 width2 = INP.feature_width2 normalize_maps_to_maximu = False trim_maps = False units = "1/cm" with qr.energy_units(units): data_descr = "_dO="+str(dE)+"_omega="+str(omega)+ \ "_HR="+str(HR)+"_J="+str(JJ) if use_vib: sys_char = "_vib" else: sys_char = "_ele" data_ext = sys_char + ".png" obj_ext = sys_char + ".qrp" # parameters of the SP if use_trimer: E2 = trimer["E2"] epsa = (E0 + E2) / 2.0 DE = trimer["DE"] J2 = 0.5 * numpy.sqrt(((E0 - E2)**2) - (DE**2)) ESP2 = epsa + DE / 2.0 ESP1 = epsa - DE / 2.0 # # Model system is a dimer of molecules # with qr.energy_units("1/cm"): if not use_trimer: if disE is not None: mol1 = qr.Molecule([0.0, E0 + disE[0]]) mol2 = qr.Molecule([0.0, E0 + dE + disE[1]]) print("Monomer 1 energy:", E0 + disE[0], "1/cm") print("Monomer 2 energy:", E0 + dE + disE[1], "1/cm") else: mol1 = qr.Molecule([0.0, E0]) mol2 = qr.Molecule([0.0, E0 + dE]) print("Monomer 1 energy:", E0, "1/cm") print("Monomer 2 energy:", E0 + dE, "1/cm") else: if disE is not None: mol1 = qr.Molecule([0.0, ESP2 + disE[0]]) mol2 = qr.Molecule([0.0, E0 + dE + disE[1]]) print("Monomer 1 (SP_high) energy:", ESP2 + disE[0], "1/cm") print("Monomer 2 (B) energy:", E0 + dE + disE[1], "1/cm") else: mol1 = qr.Molecule([0.0, ESP2]) mol2 = qr.Molecule([0.0, E0 + dE]) print("Monomer 1 (SP_high) energy:", ESP2, "1/cm") print("Monomer 2 (B) energy:", E0 + dE, "1/cm") if disE is not None: mol3 = qr.Molecule([0.0, ESP1 + disE[2]]) print("Monomer 3 (SP_low) energy:", ESP1 + disE[2], "1/cm") else: mol3 = qr.Molecule([0.0, ESP1]) print("Monomer 3 (SP_low) energy:", ESP1, "1/cm") mol3.set_transition_width((0, 1), width2) mol3.set_dipole(0, 1, trimer["dipsp"]) mol1.set_transition_width((0, 1), width2) mol1.set_dipole(0, 1, dip1) mol2.set_transition_width((0, 1), width) mol2.set_dipole(0, 1, dip2) if use_trimer: agg = qr.Aggregate([mol1, mol2, mol3]) else: agg = qr.Aggregate([mol1, mol2]) if use_trimer: with qr.energy_units("1/cm"): agg.set_resonance_coupling(0, 1, JJ) print("B - SP_high coupling:", JJ, "1/cm") agg.set_resonance_coupling(0, 2, J2) print("SP coupling:", J2, "1/cm") else: with qr.energy_units("1/cm"): agg.set_resonance_coupling(0, 1, JJ) # # Electronic only aggregate # agg_el = agg.deepcopy() # # if nuclear vibrations are to be added, do it here # if use_vib: with qr.energy_units("1/cm"): mod1 = qr.Mode(omega) mod2 = qr.Mode(omega) if vib_loc == "down": set_vib = [True, False] elif vib_loc == "up": set_vib = [False, True] elif vib_loc == "both": set_vib = [True, True] else: raise Exception("Unknown location of the vibrations") if set_vib[0]: print("Vibrations set for SP_high molecule") mol1.add_Mode(mod1) mod1.set_nmax(0, INP.vibmode["no_g_vib"]) mod1.set_nmax(1, INP.vibmode["no_e_vib"]) mod1.set_HR(1, HR) if set_vib[1]: print("Vibrations set for B molecule") mol2.add_Mode(mod2) mod2.set_nmax(0, INP.vibmode["no_g_vib"]) mod2.set_nmax(1, INP.vibmode["no_e_vib"]) mod2.set_HR(1, HR) # # Before we build the aggregate, we make its copy to have an unbuilt version # agg3 = agg.deepcopy() aggA = agg.deepcopy() # # here we build the complete aggregate and its electronic only version # agg.build(mult=1) agg_el.build(mult=1) aggA.build(mult=1) # total Hamiltonian HH = agg.get_Hamiltonian() # electronic Hamiltonian He = agg_el.get_Hamiltonian() # # Laboratory setup # lab = qr.LabSetup() lab.set_polarizations(pulse_polarizations=[X, X, X], detection_polarization=X) # # Time axis for the calculation of excited state evolution # t2_N_steps = INP.t2_N_steps t2_time_step = INP.t2_time_step time2 = qr.TimeAxis(0.0, t2_N_steps, t2_time_step) # # Containers for 2D maps with positive and negative frequencies # cont_p = qr.TwoDResponseContainer(t2axis=time2) cont_m = qr.TwoDResponseContainer(t2axis=time2) cont_tot = qr.TwoDResponseContainer(t2axis=time2) # # spectra will be indexed by the times in the time axis `time2` # cont_p.use_indexing_type(time2) cont_m.use_indexing_type(time2) cont_tot.use_indexing_type(time2) # # We define two-time axes, which will be FFTed and will define # the omega_1 and omega_3 axes of the 2D spectrum # t1_N_steps = INP.t1_N_steps t1_time_step = INP.t1_time_step t3_N_steps = INP.t3_N_steps t3_time_step = INP.t3_time_step t1axis = qr.TimeAxis(0.0, t1_N_steps, t1_time_step) t3axis = qr.TimeAxis(0.0, t3_N_steps, t3_time_step) # # This calculator calculated 2D spectra from the effective width # msc = qr.MockTwoDResponseCalculator(t1axis, time2, t3axis) with qr.energy_units("1/cm"): msc.bootstrap(rwa=E0, shape="Gaussian") # # System-bath interaction including vibrational states # operators = [] rates = [] print("Relaxation rates: ", rate, rate_sp, "1/fs") with qr.eigenbasis_of(He): if use_trimer: if He.data[3, 3] < He.data[2, 2]: Exception("Electronic states not orderred!") operators.append(qr.qm.ProjectionOperator(2, 3, dim=He.dim)) with qr.energy_units("1/cm"): print("2 <- 3 : energies = ", He.data[2, 2], He.data[3, 3], "1/cm") rates.append(rate) print("Transfer time 2 <- 3:", 1.0 / rate, "fs") if He.data[2, 2] < He.data[1, 1]: Exception("Electronic states not orderred!") operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim)) with qr.energy_units("1/cm"): print("1 <- 2 : energies = ", He.data[1, 1], He.data[2, 2], "1/cm") rates.append(rate_sp) print("Transfer time 1 <- 2", 1.0 / rate_sp, "fs") else: if He.data[2, 2] < He.data[1, 1]: Exception("Electronic states not orderred!") operators.append(qr.qm.ProjectionOperator(1, 2, dim=He.dim)) with qr.energy_units("1/cm"): print("1 <- 2 : energies = ", He.data[1, 1], He.data[2, 2], "1/cm") rates.append(rate_sp) print("Transfer time 1 <- 2", 1.0 / rate, "fs") # include detailed balace if detailed_balance: if use_trimer: with qr.eigenbasis_of(He): T = temperature #77.0 Den = (He.data[3, 3] - He.data[2, 2]) / (kB_int * T) operators.append(qr.qm.ProjectionOperator(3, 2, dim=He.dim)) thermal_fac = numpy.exp(-Den) rates.append(rate * thermal_fac) Den = (He.data[2, 2] - He.data[1, 1]) / (kB_int * T) operators.append(qr.qm.ProjectionOperator(2, 1, dim=He.dim)) thermal_fac = numpy.exp(-Den) rates.append(rate_sp * thermal_fac) else: with qr.eigenbasis_of(He): T = temperature #77.0 Den = (He.data[2, 2] - He.data[1, 1]) / (kB_int * T) operators.append(qr.qm.ProjectionOperator(2, 1, dim=He.dim)) thermal_fac = numpy.exp(-Den) rates.append(rate * thermal_fac) sbi = qr.qm.SystemBathInteraction(sys_operators=operators, rates=rates) sbi.set_system(agg) # # Lindblad form for relaxation # LF = qr.qm.ElectronicLindbladForm(HH, sbi, as_operators=True) # # Pure dephasing # p_deph = qr.qm.ElectronicPureDephasing(agg, dtype="Gaussian") # we simplify calculations by converting dephasing to # corresponding Lorentzian form p_deph.convert_to("Lorentzian") eUt = qr.qm.EvolutionSuperOperator(time2, HH, relt=LF, pdeph=p_deph, mode="all") eUt.set_dense_dt(INP.fine_splitting) print("---") # # We calculate evolution superoperator # eUt.calculate(show_progress=False) # save the evolution operator if save_eUt: eut_name = os.path.join( dname, "eUt" + "_omega2=" + str(omega) + data_descr + obj_ext) eUt.save(eut_name) # # Prepare aggregate with all states (including 2-EX band) # agg3.build(mult=2) agg3.diagonalize() agg_el.diagonalize() print("") print("Square of the transition dipoles") print(agg_el.D2) print("") print("---") # # calculation of absorption spectrum # time1 = qr.TimeAxis(0.0, 1000, 5.0) absc = qr.MockAbsSpectrumCalculator(time1, system=aggA) with qr.energy_units("1/cm"): absc.bootstrap(rwa=E0) spctrm = absc.calculate() spctrm.normalize2() with qr.energy_units("1/cm"): spctrm.plot(show=False, axis=[10500.0, 13500.0, 0.0, 1.1]) spctrm.savefig(os.path.join(dname, "abs.png")) spctrm.save_data(os.path.join(dname, "abs.dat")) pways = dict() olow_cm = omega - INP.omega_uncertainty / 2.0 ohigh_cm = omega + INP.omega_uncertainty / 2.0 olow = qr.convert(olow_cm, "1/cm", "int") ohigh = qr.convert(ohigh_cm, "1/cm", "int") for t2 in time2.data: # this could save some memory of pathways become too big pways = dict() print("T2 =", t2, "fs (of T2_max =", time2.max, "fs)") twod = msc.calculate_one_system(t2, agg3, eUt, lab, pways=pways, dtol=1.0e-12, selection=[["omega2", [olow, ohigh]]]) pws = pways[str(t2)] npa = len(pws) has_R = False has_NR = False for pw in pws: if pw.pathway_type == "NR": has_NR = True elif pw.pathway_type == "R": has_R = True if t2 in t2_save_pathways: pws_name = os.path.join( dname, "pws_t2=" + str(t2) + "_omega2=" + str(omega) + data_descr + obj_ext) qr.save_parcel(pways[str(t2)], pws_name) cont_p.set_spectrum(twod) twod = msc.calculate_one_system(t2, agg3, eUt, lab, pways=pways, dtol=1.0e-12, selection=[["omega2", [-ohigh, -olow]]]) pws = pways[str(t2)] npa = len(pws) #print(" m:", npa) has_R = False has_NR = False for pw in pws: if pw.pathway_type == "NR": has_NR = True elif pw.pathway_type == "R": has_R = True #print(" R:", has_R, ", NR:", has_NR) if t2 in t2_save_pathways: pws_name = os.path.join( dname, "pws_t2=" + str(t2) + "_omega2=" + str(-omega) + data_descr + obj_ext) qr.save_parcel(pways[str(t2)], pws_name) cont_m.set_spectrum(twod) # calculation without pre-selecting pathways twod = msc.calculate_one_system(t2, agg3, eUt, lab, pways=pways, dtol=1.0e-12) cont_tot.set_spectrum(twod) # saving total spectrum to a directory for further analysis try: saveit = INP.total_spectrum["save_it"] except: saveit = False if saveit: try: dform = INP.total_spectrum["data_format"] except: dform = "dat" try: stp = INP.total_spectrum["spectra_type"] if stp == "TOTL": stype = qr.signal_TOTL elif stp == "REPH": stype = qr.signal_REPH elif stp == "NONR": stype = qr.signal_NONR else: raise Exception("Wrong signal type") except: stype = qr.signal_REPH save_spectra(cont_tot, ext=dform, dir=dname, stype=stype) # # Save aggregate when a single calculation is done # if save_eUt: fname = os.path.join(dname, "aggregate.qrp") agg3.save(fname) # # Window function for subsequenty FFT # window = func.Tukey(time2, r=INP.tukey_window_r, sym=False) # # FFT with the window function # # Specify REPH, NONR or `total` to get different types of spectra # print("Calculating FFT of the 2D maps") #fcont = cont.fft(window=window, dtype=stype) #, dpart="real", offset=0.0) fcont_p_re = cont_p.fft(window=window, dtype=qr.signal_REPH) fcont_p_nr = cont_p.fft(window=window, dtype=qr.signal_NONR) fcont_p_to = cont_p.fft(window=window, dtype=qr.signal_TOTL) if normalize_maps_to_maximu: fcont_p_re.normalize2(dpart=qr.part_ABS) fcont_p_nr.normalize2(dpart=qr.part_ABS) fcont_p_to.normalize2(dpart=qr.part_ABS) fcont_m_re = cont_m.fft(window=window, dtype=qr.signal_REPH) fcont_m_nr = cont_m.fft(window=window, dtype=qr.signal_NONR) fcont_m_to = cont_m.fft(window=window, dtype=qr.signal_TOTL) if normalize_maps_to_maximu: fcont_m_re.normalize2(dpart=qr.part_ABS) fcont_m_nr.normalize2(dpart=qr.part_ABS) fcont_m_to.normalize2(dpart=qr.part_ABS) if trim_maps: twin = INP.trim_maps_to with qr.energy_units("1/cm"): fcont_p_re.trimall_to(window=twin) fcont_p_nr.trimall_to(window=twin) fcont_p_to.trimall_to(window=twin) show_omega = omega with qr.frequency_units("1/cm"): sp1_p_re, show_Npoint1 = fcont_p_re.get_nearest(show_omega) sp2_p_re, show_Npoint2 = fcont_p_re.get_nearest(-show_omega) sp1_p_nr, show_Npoint1 = fcont_p_nr.get_nearest(show_omega) sp2_p_nr, show_Npoint2 = fcont_p_nr.get_nearest(-show_omega) sp1_p_to, show_Npoint1 = fcont_p_to.get_nearest(show_omega) sp2_p_to, show_Npoint2 = fcont_p_to.get_nearest(-show_omega) sp1_m_re, show_Npoint1 = fcont_m_re.get_nearest(show_omega) sp2_m_re, show_Npoint2 = fcont_m_re.get_nearest(-show_omega) sp1_m_nr, show_Npoint1 = fcont_m_nr.get_nearest(show_omega) sp2_m_nr, show_Npoint2 = fcont_m_nr.get_nearest(-show_omega) sp1_m_to, show_Npoint1 = fcont_m_to.get_nearest(show_omega) sp2_m_to, show_Npoint2 = fcont_m_to.get_nearest(-show_omega) sstm = platform.system() #print(sstm) if sstm != "Windows": import resource memo = resource.getrusage( resource.RUSAGE_SELF).ru_maxrss / (1024 * 1024) print("Memory usage: ", memo, "in MB") return (sp1_p_re, sp1_p_nr, sp2_m_re, sp2_m_nr)
# # CT states are dark # PCT_M.set_dipole(1, 0, [0.0, 0.0, 0.0]) PCT_L.set_dipole(1, 0, [0.0, 0.0, 0.0]) molecules = [PM, PL, BM, BL, HL, HM, PCT_M, PCT_L] # saving molecules without environment qr.save_parcel(molecules, os.path.join(pre_out, "molecules.qrp")) # # Here we build the RC as an aggregate of molecules # mol3 = [PM, PL, BM] agg = qr.Aggregate(molecules=mol3) # # Exciton interaction matrix # # values from Ref. 1 JP_77K_Jordanides = 575.0 JP_77K = JP_77K_Jordanides # # Fitted values of the model with CT states # starting values of the manual search of best parameters are # taken from Ref. 2 # if jordanides:
with qr.energy_units("1/cm"): cfce1 = qr.CorrelationFunction(ta,cfce_params1) cfce2 = qr.CorrelationFunction(ta,cfce_params2) m1 = qr.Molecule([0.0, 12100], name="M1") m1.set_dipole(0,1,[0.0,3.0,0.0]) m1.set_transition_environment((0,1),cfce1) m1.position = [0.0,0.0,0.0] m2 = qr.Molecule([0.0, 12000], name="M2") m2.set_dipole(0,1,[0.0,1.0,1.0]) m2.position = [5.0,0.0,0.0] m2.set_transition_environment((0,1),cfce2) # create an aggregate AG = qr.Aggregate(name="TestAggregate") #AG.set_egcf_matrix(cm) # fill the cluster with monomers AG.add_Molecule(m1) AG.add_Molecule(m2) # setting coupling by dipole-dipole formula AG.set_coupling_by_dipole_dipole() AG.build() HH = AG.get_Hamiltonian() (RR,ham) = AG.get_RelaxationTensor(ta, relaxation_theory="standard_Redfield")
def calcTwoD(n_loopsum): # ''' Caculates a 2d spectrum for both perpendicular and parael lasers using aceto bands and accurate lineshapes''' resp_container = [] #energies0 = [energy - (100 * num_mol / 2) + i * 100 for i in range(num_mol)] #energies0 = [energy] * num_mol # Giving random energies to the moleucles according to a gauss dist with qr.energy_units("1/cm"): for i, mol in enumerate(for_agg): mol.set_energy(1, random.gauss(energy, static_dis)) #mol.set_energy(1, energies0[i]) agg = qr.Aggregate(molecules=for_agg) agg_rates = copy.deepcopy(agg) agg_rates.set_coupling_by_dipole_dipole(epsr=1.21) agg_rates.build(mult=2) if _forster_: KK = agg_rates.get_FoersterRateMatrix() else: KK = agg_rates.get_RedfieldRateMatrix() agg.set_coupling_by_dipole_dipole(epsr=1.21) agg.build(mult=2) rwa = agg.get_RWA_suggestion() HH = agg_rates.get_Hamiltonian() pig_ens = np.diagonal(HH.data[1:num_mol+1,1:num_mol+1])\ / (2.0*const.pi*const.c*1.0e-13) en_order = np.argsort(pig_ens) SS = HH.diagonalize() eig_vecs = np.transpose(SS[1:num_mol + 1, 1:num_mol + 1]) state_ens = np.diagonal(HH.data[1:num_mol+1,1:num_mol+1])\ / (2.0*const.pi*const.c*1.0e-13) agg_rates.diagonalize() dips = agg_rates.D2[0][1:num_mol + 1] dip_order = np.flip(np.argsort(dips)) # Initialising the twod response calculator for the paralell laser resp_calc_temp = qr.TwoDResponseCalculator(t1axis=t13_ax, t2axis=t2_ax, t3axis=t13_ax, system=agg, rate_matrix=KK) # keep_resp saves the reponse int he object. write_resp writes to numpy # Response is calculated Converted into spectrum Stored in a container for i, lab in enumerate(labs): resp_calc = copy.deepcopy(resp_calc_temp) resp_calc.bootstrap( rwa, lab=lab, verbose=False, keep_resp=True) #write_resp = save_dir + las_pol[i] + '_resp', resp_cont = resp_calc.calculate() resp_container.append(resp_calc.responses) state_data = { 'pig_ens': pig_ens, 'en_order': en_order, 'eig_vecs': eig_vecs, 'state_ens': state_ens, 'dips': dips, 'dip_order': dip_order, 'rwa': rwa } return resp_container, state_data
with qr.energy_units("1/cm"): mol1 = qr.Molecule([0.0, E1]) mol1.set_dipole(0, 1, [1.0, 0.0, 0.0]) mol1.set_transition_width((0, 1), width) mod = qr.Mode(omega) mol1.add_Mode(mod) mod.set_nmax(0, 2) mod.set_nmax(1, 2) mod.set_HR(1, HR) mol2 = qr.Molecule([0.0, E2]) mol2.set_dipole(0, 1, numpy.array([1.0, 1.0, 0.0]) / numpy.sqrt(2.0)) mol2.set_transition_width((0, 1), width) agg = qr.Aggregate(molecules=[mol1, mol2]) agg.set_resonance_coupling(0, 1, JJ) agg.build(mult=2) agg.diagonalize() H = agg.get_Hamiltonian() #with qr.energy_units("1/cm"): # print(H) lab = qr.LabSetup() lab.set_polarizations(pulse_polarizations=[X, X, X], detection_polarization=X) #print(qr.convert(agg.Wd,"int","1/cm"))
energies2 = [energy - (100 * num_mol / 2) + i * 100 for i in range(num_mol)] test_dynamics(agg_list=forAggregate, energies=energies1) test_dynamics(agg_list=forAggregate, energies=energies2) ####################################################################### # Calculation of the twoD PARA ####################################################################### # Arrays for the direction on the laser plane a_0 = np.array([1.0, 0.0, 0.0], dtype=np.float64) a_90 = np.array([0.0, 1.0, 0.0], dtype=np.float64) # Checks if mock is selected and creates agregate and laser conditions if _mock_: # Creation of aggregate from mockAggList (multiplicity must be 2) aggMock = qr.Aggregate(molecules=forAggregateMock) aggMock.set_coupling_by_dipole_dipole(epsr=1.21) aggMock.build(mult=2) aggMock.diagonalize() rwa = aggMock.get_RWA_suggestion() # quantarhei lab setup for mock calculator labParaMock = qr.LabSetup() labPerpMock = qr.LabSetup() labParaMock.set_polarizations(pulse_polarizations=[a_0,a_0,a_0], detection_polarization=a_0) labPerpMock.set_polarizations(pulse_polarizations=[a_0,a_0,a_90], detection_polarization=a_90) else: # Aggregate for twodcalculator (multiplicity must be 2) agg = qr.Aggregate(molecules=forAggregate)
# # MODEL: Simple dimer of molecules # ############################################################################### with qr.energy_units("1/cm"): # two two-level molecules m1 = qr.Molecule([0.0, 12000.0]) m2 = qr.Molecule([0.0, 12300.0]) # transitions will have Gaussian lineshape with a width specified here m1.set_transition_width((0, 1), 150.0) m2.set_transition_width((0, 1), 150.0) # we create an aggregate from the two molecules agg = qr.Aggregate(molecules=[m1, m2]) # we set transition dipole moment orientations for the two molecules m1.set_dipole(0, 1, [1.0, 0.8, 0.8]) m2.set_dipole(0, 1, [0.8, 0.8, 0.0]) # resonance coupling is set by hand with qr.energy_units("1/cm"): agg.set_resonance_coupling(0, 1, 100.0) # we copy the aggregate before it is built. For the calculation of 2D # spectrum, we need to build the aggregate so that it contains two-exciton # states. But those are irrelevant for single exciton excited state dynamics # so we make two identical aggregates, one with single-excitons only, and # one with two-excitons. agg_2D = copy.copy(agg)
mod = qr.Mode(frequency=omega) mol.add_Mode(mod) mod.set_nmax(0, Ng) mod.set_nmax(1, Ne) mod.set_HR(1, hr_factor) mol.set_transition_width((0, 1), width) # # Create an aggregate # agg = qr.Aggregate(molecules=[mol]) agg.build() # # Time axes and the calculator # t1axis = qr.TimeAxis(0.0, 1000, 10.0) t3axis = qr.TimeAxis(0.0, 1000, 10.0) t2axis = qr.TimeAxis(0.0, Nt2, dt2) # FIXME: TwoDResponseCalculator msc = qr.MockTwoDResponseCalculator(t1axis, t2axis, t3axis) msc.bootstrap(rwa=qr.convert(E1, "1/cm", "int"), shape="Gaussian") #
# with qr.energy_units("1/cm"): # molecule 1 # molecular states m1 = qr.Molecule([0.0, 10000.0]) # transition dipole moment m1.set_dipole(0, 1, [1.0, 0.0, 0.0]) # molecule 2 m2 = qr.Molecule([0.0, 11000.0]) m2.set_dipole(0, 1, [1.0, 0.0, 0.0]) # # Create aggregate of the two molecules # agg = qr.Aggregate(molecules=[m1, m2]) # # Define time span of the calculation # time = qr.TimeAxis(0.0, 10000, 1.0) # # Define bath correlation function # cpar = dict(ftype="OverdampedBrownian", cortime=30, reorg=200, T=300) with qr.energy_units("1/cm"): cf = qr.CorrelationFunction(time, cpar) # # Set the correlation function to the transitions on the molecules
## ## Set system-bath interaction ## #mol1.set_transition_environment((0,1),cf) #mol2.set_transition_environment((0,1),cf) #mol3.set_transition_environment((0,1),cf) for ii in range(Nmol): mols[ii].set_transition_environment((0,1), cf) # # Creating aggregate # #agg = Aggregate(name="Dimer", molecules=[mol1, mol2, mol3]) agg = qr.Aggregate(molecules=mols) #agg.set_coupling_by_dipole_dipole() with qr.energy_units("1/cm"): #agg.set_resonance_coupling(0,1,-100.0) #agg.set_resonance_coupling(1,2,-100.0) if Nmol > 1: for ii in range(Nmol-1): agg.set_resonance_coupling(ii,ii+1,-100.0) agg.set_resonance_coupling(0,Nmol-1,-100.0) print(agg.get_resonance_coupling(0,1)) agg.build(mult=2) print(agg.get_Hamiltonian()) with qr.energy_units("1/cm"):
# Bath correlation functions timea = qr.TimeAxis(0.0, 1000, 1.0) cfpar = dict(ftype="OverdampedBrownian", reorg=30, cortime=100, T=300, matsubara=100) with qr.energy_units("1/cm"): cf = qr.CorrelationFunction(timea, cfpar) # set bath correlation functions to the molecules for i_m in range(N_molecules): mols[i_m].set_transition_environment((0, 1), cf) # aggregate of molecules agg = qr.Aggregate(mols) agg.set_coupling_by_dipole_dipole() # Building the aggregate qr.log_report("Building aggregate") agg.build() qr.log_report("...done") qr.log_detail("Resonance coupling matrix: ") qr.log_detail(qr.convert(agg.resonance_coupling, "int", "1/cm"), use_indent=False) # Dimension of the problem HH = agg.get_Hamiltonian() Nr = HH.dim
def test_LindbladWithVibrations_dynamics_comp(self): """Compares Lindblad dynamics of a system with vibrations calculated from propagator and superoperator """ # Aggregate import quantarhei as qr time = qr.TimeAxis(0.0, 1000, 1.0) # create a model with qr.energy_units("1/cm"): me1 = qr.Molecule([0.0, 12100.0]) me2 = qr.Molecule([0.0, 12000.0]) me3 = qr.Molecule([0.0, 12900.0]) agg_el = qr.Aggregate([me1, me2, me3]) agg_el.set_resonance_coupling(0, 1, qr.convert(150, "1/cm", to="int")) agg_el.set_resonance_coupling(1, 2, qr.convert(50, "1/cm", to="int")) m1 = qr.Molecule([0.0, 12100.0]) m2 = qr.Molecule([0.0, 12000.0]) m3 = qr.Molecule([0.0, 12900.0]) mod1 = qr.Mode(frequency=qr.convert(100, "1/cm", "int")) m1.add_Mode(mod1) mod1.set_HR(1, 0.01) agg = qr.Aggregate([m1, m2, m3]) agg.set_resonance_coupling(0, 1, qr.convert(150, "1/cm", to="int")) agg.set_resonance_coupling(1, 2, qr.convert(50, "1/cm", to="int")) agg_el.build() agg.build() hame = agg_el.get_Hamiltonian() ham = agg.get_Hamiltonian() # calculate relaxation tensor with qr.eigenbasis_of(hame): # # Operator describing relaxation # K = qr.qm.ProjectionOperator(1, 2, dim=hame.dim) # # System bath interaction with prescribed rate # from quantarhei.qm import SystemBathInteraction sbi = SystemBathInteraction(sys_operators=[K], rates=(1.0 / 100.0, )) sbi.set_system(agg) #agg.set_SystemBathInteraction(sbi) with qr.eigenbasis_of(ham): # # Corresponding Lindblad form # from quantarhei.qm import ElectronicLindbladForm LF = ElectronicLindbladForm(ham, sbi, as_operators=True) # # Evolution of reduced density matrix # prop = qr.ReducedDensityMatrixPropagator(time, ham, LF) # # Evolution by superoperator # eSO = qr.qm.EvolutionSuperOperator(time, ham, LF) eSO.set_dense_dt(5) eSO.calculate() # compare the two propagations pairs = [(5, 4), (5, 5), (6, 5), (7, 5)] for p in pairs: rho_i1 = qr.ReducedDensityMatrix(dim=ham.dim) rho_i1.data[p[0], p[1]] = 1.0 rho_t1 = prop.propagate(rho_i1) exp_rho_t2 = eSO.data[:, :, :, p[0], p[1]] #import matplotlib.pyplot as plt #plt.plot(rho_t1.TimeAxis.data, numpy.real(rho_t1.data[:,p[0],p[1]]), "-r") #plt.plot(rho_t1.TimeAxis.data, numpy.real(exp_rho_t2[:,p[0],p[1]]), "--g") #plt.show() #for kk in range(rho_t1.TimeAxis.length): # print(kk, numpy.real(rho_t1.data[kk,p[0],p[1]]),numpy.real(exp_rho_t2[kk,p[0],p[1]])) #numpy.testing.assert_allclose(RRT.data, rtd) numpy.testing.assert_allclose(numpy.real(rho_t1.data[:, :, :]), numpy.real(exp_rho_t2[:, :, :]), rtol=5.0e-2, atol=1.0e-3)