def setUp(self):
# wavefunction
self.wf = RBF_HO1D(ndim=1, nelec=1, ncenter=5)
#sampler
self.mh_sampler = METROPOLIS(nwalkers=250,
nstep=1000,
step_size=3.,
nelec=self.wf.nelec,
ndim=self.wf.ndim,
domain={
'min': -5,
'max': 5
})
#sampler
self.hmc_sampler = HAMILTONIAN(nwalkers=250,
nstep=1000,
step_size=0.01,
nelec=self.wf.nelec,
ndim=self.wf.ndim,
domain={
'min': -5,
'max': 5
},
L=5)
# optimizer
self.opt = optim.Adam(self.wf.parameters(), lr=0.01)
# network
self.net = DeepQMC(wf=self.wf,
sampler=self.mh_sampler,
optimizer=self.opt)
def setUp(self):
# optimal parameters
self.opt_r = 0.97 # the two h are at +0.97 and -0.97
self.opt_sigma = 1.20
# wavefunction
centers = torch.tensor([[0.,0.,-self.opt_r],[0.,0.,self.opt_r]])
sigma = torch.tensor([self.opt_sigma,self.opt_sigma])
self.wf = RBF_H2p(centers = centers,
sigma = sigma )
#sampler
self.mh_sampler = METROPOLIS(nwalkers=1000, nstep=1000,
step_size = 0.5, nelec = self.wf.nelec,
ndim = self.wf.ndim, domain = {'min':-5,'max':5})
#sampler
self.hmc_sampler = HAMILTONIAN(nwalkers=1000, nstep=200,
step_size = 0.1, nelec = self.wf.nelec,
ndim = self.wf.ndim, domain = {'min':-5,'max':5}, L=5)
# optimizer
self.opt = optim.Adam(self.wf.parameters(),lr=0.001)
# network
self.net = DeepQMC(wf=self.wf,sampler=self.mh_sampler,optimizer=self.opt)
# ground state energy
self.ground_state_energy = -0.597
def setUp(self):
# wavefunction
self.wf = RBF_H(centers=torch.tensor([[0., 0., 0.]]),
sigma=torch.tensor([1.]))
#sampler
self.mh_sampler = METROPOLIS(nwalkers=1000,
nstep=1000,
step_size=3.,
nelec=self.wf.nelec,
ndim=self.wf.ndim,
domain={
'min': -5,
'max': 5
})
#sampler
self.hmc_sampler = HAMILTONIAN(nwalkers=1000,
nstep=1000,
step_size=0.01,
nelec=self.wf.nelec,
ndim=self.wf.ndim,
domain={
'min': -5,
'max': 5
},
L=5)
# optimizer
self.opt = optim.Adam(self.wf.parameters(), lr=0.01)
# network
self.net = DeepQMC(wf=self.wf,
sampler=self.mh_sampler,
optimizer=self.opt)
# ground state energy
self.ground_state_energy = -0.5
#sampler
sampler = METROPOLIS(nwalkers=250,
nstep=1000,
step_size=3.,
nelec=wf.nelec,
ndim=wf.ndim,
domain={
'min': -5,
'max': 5
})
# optimizer
opt = optim.Adam(wf.parameters(), lr=0.01)
# network
net = DeepQMC(wf=wf, sampler=sampler, optimizer=opt)
pos, obs_dict = None, None
# set up the plotter
plt.ion()
plot2D = plotter2d(wf, domain, [25, 25], sol=ho2d_sol, kinetic=False)
# train the wavefunction
pos, obs_dict = net.train(100,
batchsize=250,
pos=pos,
obs_dict=obs_dict,
resample=100,
ntherm=-1,
loss='variance',
plot=plot2D)
