class VMC_workflow

This class manages the workflow of VMC calculations.

Module usage

from turboworkflows.workflow_vmc import VMC_workflow

Constructor arguments

job parameters

argument	type	default value	description
server_machine_name	str	localhost	name of the server machine for job submission.
queue_label	str	(None)	queue label for job submission.
mpi	bool	False	use MPI parallelization.
version	str	stable	version of packages to use.
sleep_time	int	1800	interval of job status check in seconds.

vmc parameters

argument	type	default value	description
vmc_rerun	bool	False	if True, force rerun even if pickle file exists.
vmc_max_continuation	int	2	maximum number of continuation runs.
vmc_target_error_bar	float	2.0e-5	target error bar in Ha.
vmc_trial_steps	int	150	number of trial steps for initial run.
vmc_safe_trial_steps	bool	True	use safe minimum trial steps.
vmc_bin_block	int	10	binning length
vmc_warmupblocks	int	5	number of disregarded blocks,
vmc_num_walkers	int	-1	number of walkers, -1 (default) = the number of MPI processes
vmc_twist_average	bool	False	flag for twist average
vmc_kpoints	list	(None)	Monkhorst-Pack k-grids, [kx,ky,kz,nx,ny,nz], kx,ky,kz for grids, nx,ny,nz for shift=0, noshift=1.
vmc_force_calc_flag	bool	False	if True, compute energy and force, if False, compute only energy
vmc_maxtime	int	172000	maximum time (sec.)

Description

In the VMC_workflow class, the VMC (Variational Monte Carlo) workflow is executed asynchronously (via async_launch()). The workflow runs VMC in a continuation loop (up to vmc_max_continuation runs). For each continuation index \(i_{\mathrm{cont}}\):

test run (icont = 0)

Uses fixed trial steps (vmc_trial_steps) to produce energy and error-bar data. Optionally enforces a minimum trial length when vmc_safe_trial_steps is True (40 blocks plus warmup).
No step estimation is performed.
Results are used by the next run to estimate required VMC steps.

production run (icont ≥ 1)

Loads the previous run's results from the corresponding pkl file and reads the number of MCMC steps from fort.12 (with twist-averaging adjustment when vmc_twist_average is enabled).
If the current error bar is below 1.1 × vmc_target_error_bar, the workflow exits early and returns the current energy and error (target achieved).
Otherwise, estimates the total steps required to reach vmc_target_error_bar via

\[N_{\mathrm{total}} \propto \left( \frac{\sigma}{E_{\mathrm{target}}} \right)^2\]

then sets the additional steps for this run to \(\max(N_{\mathrm{total}} - N_{\mathrm{done}}, 1)\).
Estimates runtime from the previous run's estimated_time_for_1_generation and logs it.
Builds a VMC_genius instance with the (additional) vmcsteps, generates input, and submits the job.

For each run, the workflow:

Waits for job submission and completion using asyncio.sleep().
Fetches output files (e.g. fort.11, fort.12, out_vmc_*, parminimized.d).
Computes energy and forces (compute_energy_and_forces()), then stores results with the configured vmc_bin_block and vmc_warmupblocks.
Persists state in pkl files under the pkl directory.

If all continuation pkl files already exist and vmc_rerun is False, the calculation is skipped. On success, the method returns (status, list of output file paths under the root directory, and an output-values dict containing "energy" and "error").