def setUp(self): torch.manual_seed(0) np.random.seed(0) set_torch_double_precision() # molecule self.mol = Molecule(atom='Li 0 0 0; H 0 0 3.015', unit='bohr', calculator='pyscf', basis='sto-3g') # wave function self.wf = SlaterJastrowBackFlow(self.mol, kinetic='jacobi', configs='single_double(2,2)', backflow_kernel=BackFlowKernelPowerSum, orbital_dependent_backflow=False, include_all_mo=True) # fc weights self.wf.fc.weight.data = torch.rand(self.wf.fc.weight.shape) # jastrow weights self.wf.jastrow.jastrow_kernel.weight.data = torch.rand( self.wf.jastrow.jastrow_kernel.weight.shape) # sampler self.sampler = Metropolis(nwalkers=500, nstep=200, step_size=0.05, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) # optimizer self.opt = optim.Adam(self.wf.parameters(), lr=0.01) # solver self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt) # artificial pos self.nbatch = 10 self.pos = torch.as_tensor( np.random.rand(self.nbatch, self.wf.nelec * 3)) self.pos.requires_grad = True
def setUp(self): torch.manual_seed(0) np.random.seed(0) # optimal parameters self.opt_r = 0.69 # the two h are at +0.69 and -0.69 self.opt_sigma = 1.24 # molecule self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69', unit='bohr', calculator='pyscf', basis='sto-3g') # wave function self.wf = SlaterJastrow(self.mol, kinetic='auto', configs='single(2,2)') # sampler self.sampler = Metropolis(nwalkers=1000, nstep=2000, step_size=0.5, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) self.hmc_sampler = Hamiltonian(nwalkers=100, nstep=200, step_size=0.1, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal')) # optimizer self.opt = optim.Adam(self.wf.parameters(), lr=0.01) # solver self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt) # ground state energy self.ground_state_energy = -1.16 # ground state pos self.ground_state_pos = 0.69
def setUp(self): torch.manual_seed(0) np.random.seed(0) set_torch_double_precision() # optimal parameters self.opt_r = 0.69 # the two h are at +0.69 and -0.69 self.opt_sigma = 1.24 # molecule self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69', unit='bohr', calculator='pyscf', basis='sto-3g') # wave function self.wf = SlaterOrbitalDependentJastrow( self.mol, kinetic='auto', configs='cas(2,2)', jastrow_kernel=FullyConnectedJastrowKernel, include_all_mo=True) # sampler self.sampler = Metropolis(nwalkers=1000, nstep=2000, step_size=0.5, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) # optimizer self.opt = optim.Adam(self.wf.parameters(), lr=0.01) # solver self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt) # ground state energy self.ground_state_energy = -1.16 # ground state pos self.ground_state_pos = 0.69
def setUp(self): torch.manual_seed(0) np.random.seed(0) set_torch_double_precision() # molecule path_hdf5 = (PATH_TEST / 'hdf5/LiH_adf_dz.hdf5').absolute().as_posix() self.mol = Molecule(load=path_hdf5) # wave function self.wf = SlaterOrbitalDependentJastrow(self.mol, kinetic='jacobi', configs='cas(2,2)', include_all_mo=True) # fc weights self.wf.fc.weight.data = torch.rand(self.wf.fc.weight.shape) # jastrow weights for ker in self.wf.jastrow.jastrow_kernel.jastrow_functions: ker.weight.data = torch.rand(1) # sampler self.sampler = Metropolis(nwalkers=500, nstep=200, step_size=0.05, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) # optimizer self.opt = optim.Adam(self.wf.parameters(), lr=0.01) # solver self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt) # artificial pos self.nbatch = 10 self.pos = torch.as_tensor( np.random.rand(self.nbatch, self.wf.nelec * 3)) self.pos.requires_grad = True
def setUp(self): torch.manual_seed(0) np.random.seed(0) # optimal parameters self.opt_r = 0.69 # the two h are at +0.69 and -0.69 self.opt_sigma = 1.24 # molecule self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69', unit='bohr', calculator='pyscf', basis='sto-3g') # wave function self.wf = SlaterJastrow(self.mol, kinetic='jacobi', configs='single(2,2)') # sampler self.sampler = Metropolis(nwalkers=100, nstep=500, step_size=0.5, ndim=self.wf.ndim, nelec=self.wf.nelec, ntherm=0, ndecor=1, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) # optimizer self.opt = optim.Adam(self.wf.parameters(), lr=0.01) # solver self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt)
def setUp(self): torch.manual_seed(0) # molecule path_hdf5 = (PATH_TEST / 'hdf5/H2_adf_dzp.hdf5').absolute().as_posix() self.mol = Molecule(load=path_hdf5) # wave function self.wf = SlaterJastrow(self.mol, kinetic='auto', configs='single(2,2)') # sampler self.sampler = Metropolis(nwalkers=1000, nstep=2000, step_size=0.5, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) # optimizer self.opt = optim.Adam(self.wf.parameters(), lr=0.01) # solver self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt) # ground state energy self.ground_state_energy = -1.16 # ground state pos self.ground_state_pos = 0.69
def setUp(self): torch.manual_seed(0) np.random.seed(0) # molecule self.mol = Molecule(atom='Li 0 0 0; H 0 0 3.015', unit='bohr', calculator='pyscf', basis='sto-3g') # wave function self.wf = SlaterJastrow(self.mol, kinetic='jacobi', configs='single(2,2)', include_all_mo=False) # sampler self.sampler = Metropolis(nwalkers=500, nstep=200, step_size=0.05, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) # optimizer self.opt = optim.Adam(self.wf.parameters(), lr=0.01) # solver self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt)
# define the wave function wf = SlaterJastrow(mol, kinetic='jacobi', configs='ground_state') # sampler sampler = Metropolis(nwalkers=100, nstep=500, step_size=0.25, nelec=wf.nelec, ndim=wf.ndim, init=mol.domain('atomic'), move={ 'type': 'one-elec', 'proba': 'normal' }) # solver solver = SolverSlaterJastrow(wf=wf, sampler=sampler) # single point obs = solver.single_point() # reconfigure sampler solver.sampler.ntherm = 0 solver.sampler.ndecor = 5 # compute the sampling traj pos = solver.sampler(solver.wf.pdf) obs = solver.sampling_traj(pos) plot_walkers_traj(obs.local_energy, walkers='mean')
'lr': 1E-6 }, { 'params': wf.mo.parameters(), 'lr': 1E-3 }, { 'params': wf.fc.parameters(), 'lr': 2E-3 }] opt = optim.Adam(lr_dict, lr=1E-3) # scheduler scheduler = optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.90) # QMC solver solver = SolverSlaterJastrow(wf=wf, sampler=sampler, optimizer=opt, scheduler=None) # perform a single point calculation obs = solver.single_point() # optimize the wave function # configure the solver solver.configure(track=['local_energy'], freeze=['ao', 'mo'], loss='energy', grad='auto', ortho_mo=False, clip_loss=False, resampling={ 'mode': 'update',
def setUp(self): set_torch_double_precision() reset_generator() # molecule self.mol = Molecule(atom='Li 0 0 0; H 0 0 3.015', unit='bohr', calculator='pyscf', basis='sto-3g') # molecule self.mol_ref = Molecule(atom='Li 0 0 0; H 0 0 3.015', unit='bohr', calculator='pyscf', basis='sto-3g') # backflow wave function self.wf = SlaterJastrowBackFlow(self.mol, kinetic='jacobi', configs='single_double(2,2)', include_all_mo=True) self.wf.ao.backflow_trans.backflow_kernel.weight.data *= 0. self.wf.ao.backflow_trans.backflow_kernel.weight.requires_grad = False # normal wave function self.wf_ref = SlaterJastrow(self.mol_ref, kinetic='jacobi', include_all_mo=True, configs='single_double(2,2)') # fc weights self.random_fc_weight = torch.rand(self.wf.fc.weight.shape) self.wf.fc.weight.data = self.random_fc_weight.clone() self.wf_ref.fc.weight.data = self.random_fc_weight.clone() # jastrow weights self.random_jastrow_weight = torch.rand( self.wf.jastrow.jastrow_kernel.weight.shape) self.wf.jastrow.jastrow_kernel.weight.data = self.random_jastrow_weight.clone( ) self.wf_ref.jastrow.jastrow_kernel.weight.data = self.random_jastrow_weight.clone( ) reset_generator() # sampler self.sampler = Metropolis(nwalkers=5, nstep=200, step_size=0.05, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) reset_generator() self.sampler_ref = Metropolis(nwalkers=5, nstep=200, step_size=0.05, ndim=self.wf.ndim, nelec=self.wf.nelec, init=self.mol.domain('normal'), move={ 'type': 'all-elec', 'proba': 'normal' }) # optimizer reset_generator() self.opt = optim.Adam(self.wf.parameters(), lr=0.01) reset_generator() self.opt_ref = optim.Adam(self.wf_ref.parameters(), lr=0.01) # solver self.solver_ref = SolverSlaterJastrow(wf=self.wf_ref, sampler=self.sampler_ref, optimizer=self.opt_ref) self.solver = SolverSlaterJastrow(wf=self.wf, sampler=self.sampler, optimizer=self.opt) # artificial pos self.nbatch = 10 self.pos = torch.as_tensor( np.random.rand(self.nbatch, self.wf.nelec * 3)) self.pos.requires_grad = True