qmctorch.solver.solver_slater_jastrow module

class qmctorch.solver.solver.Solver(wf=None, sampler=None, optimizer=None, scheduler=None, output=None, rank=0)

Bases: qmctorch.solver.solver_base.SolverBase

Basic QMC solver

Parameters:

• wf (qmctorch.WaveFunction, optional) – wave function. Defaults to None.
• sampler (qmctorch.sampler, optional) – Sampler. Defaults to None.
• optimizer (torch.optim, optional) – optimizer. Defaults to None.
• scheduler (torch.optim, optional) – scheduler. Defaults to None.
• output (str, optional) – hdf5 filename. Defaults to None.
• rank (int, optional) – rank of he process. Defaults to 0.

configure(track=None, freeze=None, loss=None, grad=None, ortho_mo=None, clip_loss=False, resampling=None)

Configure the solver

Parameters:

• track (list, optional) – list of observable to track. Defaults to [‘local_energy’].
• freeze ([type], optional) – list of parameters to freeze. Defaults to None.
• loss (str, optional) – merhod to compute the loss: variance or energy. Defaults to ‘energy’.
• grad (str, optional) – method to compute the gradients: ‘auto’ or ‘manual’. Defaults to ‘auto’.
• ortho_mo (bool, optional) – apply regularization to orthogonalize the MOs. Defaults to False.
• clip_loss (bool, optional) – Clip the loss values at +/- X std. X defined in Loss as clip_num_std (default 5) Defaults to False.

set_params_requires_grad(wf_params=True, geo_params=False)

Configure parameters for wf opt.

freeze_parameters(freeze)

Freeze the optimization of specified params.

Parameters:freeze (list) – list of param to freeze

save_sampling_parameters(pos)

save the sampling params.

restore_sampling_parameters()

restore sampling params to their original values.

geo_opt(nepoch, geo_lr=0.01, batchsize=None, nepoch_wf_init=100, nepoch_wf_update=50, hdf5_group='geo_opt', chkpt_every=None, tqdm=False)

optimize the geometry of the molecule

Parameters:

• nepoch (int) – Number of optimziation step
• batchsize (int, optional) – Number of sample in a mini batch. If None, all samples are used. Defaults to Never.
• hdf5_group (str, optional) – name of the hdf5 group where to store the data. Defaults to ‘geo_opt’.
• chkpt_every (int, optional) – save a checkpoint every every iteration. Defaults to half the number of epoch

run(nepoch, batchsize=None, hdf5_group='wf_opt', chkpt_every=None, tqdm=False)

Run a wave function optimization

Parameters:

• nepoch (int) – Number of optimziation step
• batchsize (int, optional) – Number of sample in a mini batch. If None, all samples are used. Defaults to Never.
• hdf5_group (str, optional) – name of the hdf5 group where to store the data. Defaults to ‘wf_opt’.
• chkpt_every (int, optional) – save a checkpoint every every iteration. Defaults to half the number of epoch

prepare_optimization(batchsize, chkpt_every, tqdm=False)

Prepare the optimization process

Parameters:

• batchsize (int or None) – batchsize
• chkpt_every (int or none) – save a chkpt file every

save_data(hdf5_group)

Save the data to hdf5.

Parameters:hdf5_group (str) – name of group in the hdf5 file

run_epochs(nepoch)

Run a certain number of epochs

Parameters:nepoch (int) – number of epoch to run

evaluate_grad_auto(lpos)

Evaluate the gradient using automatic differentiation

Parameters:lpos (torch.tensor) – sampling points Returns:loss values and local energies Return type:tuple

evaluate_grad_manual(lpos)

Evaluate the gradient using low variance expression

Parameters:

• lpos (torch.tensor) – [description]
• lpos – sampling points

Returns:

loss values and local energies

Return type:

tuple

log_data_opt(nepoch, task)

Log data for the optimization.