Arguments of the input script

5. Arguments of the input script#

Note

Although there are a lot of parameters, many of them are duplicated. There are two main parts: one is the parameters passed to dflow (dflow_config, dflow_s3_config, parallelism); The other is AESP’s own parameters (aesp_config).

dflow_config:#
type: dict | NoneType, optional, default: None
argument path: dflow_config

The configuration passed to dflow

dflow_s3_config:#
type: dict | NoneType, optional, default: None
argument path: dflow_s3_config

The S3 configuration passed to dflow

parallelism:#
type: int | NoneType, optional, default: None
argument path: parallelism

Maximum number of running pods for the workflow.

aesp_config:#
type: dict
argument path: aesp_config

Configuration of AESP

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: std-sp
argument path: aesp_config/type
possible choices: std-sp

Different structure prediction workflow in AESP.

  • std-sp: Standard workflow based on interatomic potentials or quantum chemistry methods.

When type is set to std-sp:

Standard workflow based on interatomic potentials or quantum chemistry methods.

opt_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params

Configuration of optimization algorithms.

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: std
argument path: aesp_config[std-sp]/opt_params/type
possible choices: std

Evolutionary algorithms based on different principles.

  • std: Evolutionary algorithms based on each generation.

When type is set to std:

Evolutionary algorithms based on each generation.

generation:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/generation

Evolutionary algorithms are an iterative process, and each iteration is called a generation.

gen_size:#
type: int, optional, default: 50
argument path: aesp_config[std-sp]/opt_params[std]/generation/gen_size

The size of the generated structures in initial generation.

adaptive:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/generation/adaptive

Adaptive mode

size_change_ratio:#
type: float, optional, default: 0.5
argument path: aesp_config[std-sp]/opt_params[std]/generation/adaptive/size_change_ratio

The variable proportion of structure generation in each generation.

population:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/population

In a evolutionary algorithm, a population is a collection of individuals.

pop_size:#
type: int, optional, default: 40
argument path: aesp_config[std-sp]/opt_params[std]/population/pop_size

Population size in initial generation.

adaptive:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/population/adaptive

Adaptive adjustment configuration

size_change_ratio:#
type: float, optional, default: 0.5
argument path: aesp_config[std-sp]/opt_params[std]/population/adaptive/size_change_ratio

The variable proportion of population size in each generation.

operator:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator

The operator of structure generation.

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: bulk
argument path: aesp_config[std-sp]/opt_params[std]/operator/type
possible choices: bulk

The type of the operator, i.e. the type of structure to be predicted.

When type is set to bulk:

Bulk (3D)

generator:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator

Generator

prob:#
type: float, optional, default: 0.33
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/prob

Probability of generator

random_gen_prob:#
type: float, optional, default: 1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/random_gen_prob

Probability of random generator

random_gen_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/random_gen_params

Configuration of random generator.

composition:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/random_gen_params/composition

Compositions of the structure to be predicted

spgnum:#
type: list, optional, default: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/random_gen_params/spgnum

Space group number (1-230)

factor:#
type: float, optional, default: 1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/random_gen_params/factor

Volume factor used to generate the crystal

thickness:#
type: float | NoneType, optional, default: None
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/random_gen_params/thickness

Thickness

max_count:#
type: float, optional, default: 50
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/generator/random_gen_params/max_count

Maximum number of attempts

crossover:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover

Crossover

prob:#
type: float, optional, default: 0.33
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/prob

Probability of crossover

plane_cross_prob:#
type: float, optional, default: 0.33
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/plane_cross_prob

Probability of plane crossover

sphere_cross_prob:#
type: float, optional, default: 0.33
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/sphere_cross_prob

Probability of sphere crossover

cylinder_cross_prob:#
type: float, optional, default: 0.34
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/cylinder_cross_prob

Probability of cylinder crossover

plane_cross_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/plane_cross_params

Configuration of plane crossover

stddev:#
type: float, optional, default: 0.1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/plane_cross_params/stddev

Standard deviation of the Gaussian distribution

max_count:#
type: int, optional, default: 100
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/plane_cross_params/max_count

Maximum number of attempts

sphere_cross_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/sphere_cross_params

Configuration of sphere crossover

stddev:#
type: float, optional, default: 0.1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/sphere_cross_params/stddev

Standard deviation of the Gaussian distribution

max_count:#
type: int, optional, default: 100
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/sphere_cross_params/max_count

Maximum number of attempts

cylinder_cross_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/cylinder_cross_params

Configuration of cylinder crossover

stddev:#
type: float, optional, default: 0.1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/cylinder_cross_params/stddev

Standard deviation of the Gaussian distribution

max_count:#
type: int, optional, default: 100
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/crossover/cylinder_cross_params/max_count

Maximum number of attempts

mutation:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation

Mutation

continuous_mut_factor:#
type: float, optional, default: 1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/continuous_mut_factor

Continuous mutation factor (0-4)

prob:#
type: float, optional, default: 0.34
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/prob

Probability of mutation

strain_mut_prob:#
type: float, optional, default: 0.33
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/strain_mut_prob

Probability of strain Mutation

permutation_mut_prob:#
type: float, optional, default: 0.33
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/permutation_mut_prob

Probability of permutation Mutation

ripple_mut_prob:#
type: float, optional, default: 0.34
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/ripple_mut_prob

Probability of ripple Mutation

strain_mut_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/strain_mut_params

Configuration of strain Mutation

stddev:#
type: float, optional, default: 0.1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/strain_mut_params/stddev

Standard deviation of the Gaussian distribution

max_count:#
type: int, optional, default: 100
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/strain_mut_params/max_count

Maximum number of attempts

permutation_mut_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/permutation_mut_params

Configuration of permutation Mutation

max_count:#
type: int, optional, default: 100
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/permutation_mut_params/max_count

Maximum number of attempts

ripple_mut_params:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/ripple_mut_params

Configuration of ripple Mutation

max_count:#
type: int, optional, default: 100
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/ripple_mut_params/max_count

Maximum number of attempts

miu:#
type: int, optional, default: 2
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/ripple_mut_params/miu

miu

rho:#
type: float, optional, default: 0.3
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/ripple_mut_params/rho

rho

eta:#
type: int, optional, default: 1
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/mutation/ripple_mut_params/eta

eta

adaptive:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/adaptive

Adaptive adjustment of probabilities for different operators

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: adjustment
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/adaptive/type
possible choices: adjustment, distribution

Adaptive mode

When type is set to adjustment:

Adjustment mode

factor:#
type: float, optional, default: 0.5
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/adaptive[adjustment]/factor

Scaling factor that control the magnitude of change in adaptive (0-1)

use_recent_gen:#
type: int, optional, default: 2
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/adaptive[adjustment]/use_recent_gen

Use of information from recent generations

When type is set to distribution:

Distribution model

use_recent_gen:#
type: int, optional, default: 2
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/adaptive[distribution]/use_recent_gen

Use of information from recent generations

hard_constrains:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains

Hard constraints on structure

alpha:#
type: float | list, optional, default: [20, 160]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/alpha

Angle (alpha)

beta:#
type: float | list, optional, default: [20, 160]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/beta

Angle (beta)

gamma:#
type: float | list, optional, default: [20, 160]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/gamma

Angle (gamma)

chi:#
type: float | list, optional, default: [20, 160]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/chi

Dihedral angle (chi)

psi:#
type: float | list, optional, default: [20, 160]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/psi

Dihedral angle (psi)

phi:#
type: float | list, optional, default: [20, 160]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/phi

Dihedral angle (phi)

a:#
type: float | list, optional, default: [0, 100]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/a

Lattice constant (a)

b:#
type: float | list, optional, default: [0, 100]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/b

Lattice constant (b)

c:#
type: float | list, optional, default: [0, 100]
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/c

Lattice constant (c)

tol_matrix:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/tol_matrix

Tolerance matrix of the structure

tuples:#
type: NoneType | list, optional, default: None
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/tol_matrix/tuples

Setting the minimum distance limit between atoms.

prototype:#
type: str, optional, default: atomic
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/tol_matrix/prototype

Types of generated structures.

factor:#
type: float, optional, default: 1.0
argument path: aesp_config[std-sp]/opt_params[std]/operator[bulk]/hard_constrains/tol_matrix/factor

Scaling factor for minimum distance limit.

cvg_criterion:#
type: dict
argument path: aesp_config[std-sp]/opt_params[std]/cvg_criterion

Convergence criteria for evolutionary algorithms

max_gen_num:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/opt_params[std]/cvg_criterion/max_gen_num

Maximum number of generations of evolutionary algorithms

continuous_opt_num:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/opt_params[std]/cvg_criterion/continuous_opt_num

Maximum number of generations for which the optimal structure remains constant

seeds:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/opt_params[std]/seeds

File path of random seed, i.e. the customized initial structure file.

calc_stages:#
type: list
argument path: aesp_config[std-sp]/calc_stages

Configuration of the calculation stages.

This argument takes a list with each element containing the following:

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key)
argument path: aesp_config[std-sp]/calc_stages/type
possible choices: matgl, dpmd, gulp, emt, vasp, gaussian, fpop_abacus, fpop_cp2k

The type of calculation stage.

When type is set to matgl:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[matgl]/inputs_config

The configuration for preparing inputs.

relax_cell:#
type: bool, optional, default: True
argument path: aesp_config[std-sp]/calc_stages[matgl]/inputs_config/relax_cell

Whether to optimize the crystal cell

step_max:#
type: int, optional, default: 1000
argument path: aesp_config[std-sp]/calc_stages[matgl]/inputs_config/step_max

Maximum number of steps for structural relaxation.

f_max:#
type: float, optional, default: 0.05
argument path: aesp_config[std-sp]/calc_stages[matgl]/inputs_config/f_max

Force convergence conditions for structural relaxation

model:#
type: str, optional, default: M3GNet-MP-2021.2.8-PES
argument path: aesp_config[std-sp]/calc_stages[matgl]/inputs_config/model

Model used by Matgl

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[matgl]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: python
argument path: aesp_config[std-sp]/calc_stages[matgl]/run_config/command

The run command of Matgl

out:#
type: str, optional, default: data
argument path: aesp_config[std-sp]/calc_stages[matgl]/run_config/out

The output dir name of labeled data. In deepmd/npy format provided by dpdata.

log:#
type: str, optional, default: calc.log
argument path: aesp_config[std-sp]/calc_stages[matgl]/run_config/log

The log file name of Matgl

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[matgl]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[matgl]/pstress

The external pressure in GPa

When type is set to dpmd:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[dpmd]/inputs_config

The configuration for preparing inputs.

relax_cell:#
type: bool, optional, default: True
argument path: aesp_config[std-sp]/calc_stages[dpmd]/inputs_config/relax_cell

Whether to optimize the crystal cell

step_max:#
type: int, optional, default: 1000
argument path: aesp_config[std-sp]/calc_stages[dpmd]/inputs_config/step_max

Maximum number of steps for structural relaxation.

f_max:#
type: float, optional, default: 0.05
argument path: aesp_config[std-sp]/calc_stages[dpmd]/inputs_config/f_max

Force convergence conditions for structural relaxation

model:#
type: str, optional, default: frozen_model.pb
argument path: aesp_config[std-sp]/calc_stages[dpmd]/inputs_config/model

Model used by DeepMD

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[dpmd]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: python
argument path: aesp_config[std-sp]/calc_stages[dpmd]/run_config/command

The run command of DeepMD

out:#
type: str, optional, default: data
argument path: aesp_config[std-sp]/calc_stages[dpmd]/run_config/out

The output dir name of labeled data. In deepmd/npy format provided by dpdata.

log:#
type: str, optional, default: calc.log
argument path: aesp_config[std-sp]/calc_stages[dpmd]/run_config/log

The log file name of DeepMD

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[dpmd]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[dpmd]/pstress

The external pressure in GPa

When type is set to gulp:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[gulp]/inputs_config

The configuration for preparing inputs.

kw_file:#
type: str
argument path: aesp_config[std-sp]/calc_stages[gulp]/inputs_config/kw_file

The path to the template incar file

pp_file:#
type: str
argument path: aesp_config[std-sp]/calc_stages[gulp]/inputs_config/pp_file

The pseudopotential files set by a dict, e.g. {“Al” : “path/to/the/al/pp/file”, “Mg” : “path/to/the/mg/pp/file”}

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[gulp]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: python
argument path: aesp_config[std-sp]/calc_stages[gulp]/run_config/command

The command of Gulp

out:#
type: str, optional, default: data
argument path: aesp_config[std-sp]/calc_stages[gulp]/run_config/out

The output dir name of labeled data. In deepmd/npy format provided by dpdata.

log:#
type: str, optional, default: calc.log
argument path: aesp_config[std-sp]/calc_stages[gulp]/run_config/log

The log file name of Gulp

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[gulp]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[gulp]/pstress

The external pressure in GPa

When type is set to emt:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[emt]/inputs_config

The configuration for preparing inputs.

relax_cell:#
type: bool, optional, default: True
argument path: aesp_config[std-sp]/calc_stages[emt]/inputs_config/relax_cell

Whether to optimize the crystal cell

step_max:#
type: int, optional, default: 1000
argument path: aesp_config[std-sp]/calc_stages[emt]/inputs_config/step_max

Maximum number of steps for structural relaxation.

f_max:#
type: float, optional, default: 0.05
argument path: aesp_config[std-sp]/calc_stages[emt]/inputs_config/f_max

Force convergence conditions for structural relaxation

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[emt]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: python
argument path: aesp_config[std-sp]/calc_stages[emt]/run_config/command

The run command of Emt

out:#
type: str, optional, default: data
argument path: aesp_config[std-sp]/calc_stages[emt]/run_config/out

The output dir name of labeled data. In deepmd/npy format provided by dpdata.

log:#
type: str, optional, default: calc.log
argument path: aesp_config[std-sp]/calc_stages[emt]/run_config/log

The log file name of Emt

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[emt]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[emt]/pstress

The external pressure in GPa

When type is set to vasp:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[vasp]/inputs_config

The configuration for preparing inputs.

incar:#
type: str
argument path: aesp_config[std-sp]/calc_stages[vasp]/inputs_config/incar

The path to the template incar file

pp_files:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[vasp]/inputs_config/pp_files

The pseudopotential files set by a dict, e.g. {“Al” : “path/to/the/al/pp/file”, “Mg” : “path/to/the/mg/pp/file”}

kspacing:#
type: float
argument path: aesp_config[std-sp]/calc_stages[vasp]/inputs_config/kspacing

The spacing of k-point sampling. ksapcing will overwrite the incar template

kgamma:#
type: bool, optional, default: True
argument path: aesp_config[std-sp]/calc_stages[vasp]/inputs_config/kgamma

If the k-mesh includes the gamma point. kgamma will overwrite the incar template

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[vasp]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: vasp
argument path: aesp_config[std-sp]/calc_stages[vasp]/run_config/command

The command of VASP

out:#
type: str, optional, default: data
argument path: aesp_config[std-sp]/calc_stages[vasp]/run_config/out

The output dir name of labeled data. In deepmd/npy format provided by dpdata.

log:#
type: str, optional, default: calc.log
argument path: aesp_config[std-sp]/calc_stages[vasp]/run_config/log

The log file name of VASP

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[vasp]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[vasp]/pstress

The external pressure in GPa

When type is set to gaussian:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config

The configuration for preparing inputs.

keywords:#
type: str | list
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config/keywords

Gaussian keywords, e.g. force b3lyp/6-31g**. If a list, run multiple steps.

multiplicity:#
type: int | str, optional, default: auto
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config/multiplicity

spin multiplicity state. It can be a number. If auto, multiplicity will be detected automatically, with the following rules:

fragment_guesses=True multiplicity will +1 for each radical, and +2 for each oxygen molecule

fragment_guesses=False multiplicity will be 1 or 2, but +2 for each oxygen molecule.

charge:#
type: int, optional, default: 0
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config/charge

molecule charge. Only used when charge is not provided by the system

basis_set:#
type: str, optional
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config/basis_set

custom basis set

keywords_high_multiplicity:#
type: str, optional
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config/keywords_high_multiplicity

keywords for points with multiple raicals. multiplicity should be auto. If not set, fallback to normal keywords

fragment_guesses:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config/fragment_guesses

initial guess generated from fragment guesses. If True, multiplicity should be auto

nproc:#
type: int, optional, default: 1
argument path: aesp_config[std-sp]/calc_stages[gaussian]/inputs_config/nproc

Number of CPUs to use

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[gaussian]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: g16
argument path: aesp_config[std-sp]/calc_stages[gaussian]/run_config/command

The command of Gaussian

out:#
type: str, optional, default: data
argument path: aesp_config[std-sp]/calc_stages[gaussian]/run_config/out

The output dir name of labeled data. In deepmd/npy format provided by dpdata.

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[gaussian]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[gaussian]/pstress

The external pressure in GPa

When type is set to fpop_abacus:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config

The configuration for preparing inputs.

input_file:#
type: str
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config/input_file

A template INPUT file.

pp_files:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config/pp_files

The pseudopotential files for the elements. For example: {“H”: “/path/to/H.upf”, “O”: “/path/to/O.upf”}.

element_mass:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config/element_mass

Specify the mass of some elements. For example: {“H”: 1.0079, “O”: 15.9994}.

kpt_file:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config/kpt_file

The KPT file, by default None.

orb_files:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config/orb_files

The numerical orbital fiels for the elements, by default None. For example: {“H”: “/path/to/H.orb”, “O”: “/path/to/O.orb”}.

deepks_descriptor:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config/deepks_descriptor

The deepks descriptor file, by default None.

deepks_model:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/inputs_config/deepks_model

The deepks model file, by default None.

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: abacus
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/run_config/command

The command of abacus

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[fpop_abacus]/pstress

The external pressure in GPa

When type is set to fpop_cp2k:

inputs_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[fpop_cp2k]/inputs_config

The configuration for preparing inputs.

inp_file:#
type: str
argument path: aesp_config[std-sp]/calc_stages[fpop_cp2k]/inputs_config/inp_file

The path to the user-submitted CP2K input file.

run_config:#
type: dict
argument path: aesp_config[std-sp]/calc_stages[fpop_cp2k]/run_config

The configuration for running tasks.

command:#
type: str, optional, default: cp2k
argument path: aesp_config[std-sp]/calc_stages[fpop_cp2k]/run_config/command

The command of cp2k

task_max:#
type: int, optional, default: 10
argument path: aesp_config[std-sp]/calc_stages[fpop_cp2k]/task_max

Maximum number of calculation tasks for each iteration.

pstress:#
type: float, optional, default: 0.0
argument path: aesp_config[std-sp]/calc_stages[fpop_cp2k]/pstress

The external pressure in GPa

default_step_config:#
type: dict, optional, default: {}
argument path: aesp_config[std-sp]/default_step_config

The default step configuration.

template_config:#
type: dict, optional, default: {'image': None}
argument path: aesp_config[std-sp]/default_step_config/template_config

The configs passed to the PythonOPTemplate.

image:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/template_config/image

The image to run the step.

timeout:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/template_config/retry_on_transient_error

The number of retry times if a TransientError is raised.

timeout_as_transient_error:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/default_step_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/template_config/envs

The environmental variables.

template_slice_config:#
type: dict, optional
argument path: aesp_config[std-sp]/default_step_config/template_slice_config

The configs passed to the Slices.

group_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/template_slice_config/group_size

The number of tasks running on a single node. It is efficient for a large number of short tasks.

pool_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/template_slice_config/pool_size

The number of tasks running at the same time on one node.

continue_on_failed:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/default_step_config/continue_on_failed

If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).

continue_on_num_success:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/continue_on_num_success

Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.

continue_on_success_ratio:#
type: float | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/continue_on_success_ratio

Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.

parallelism:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/parallelism

The parallelism for the step

executor:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/default_step_config/executor

The executor of the step.

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: dispatcher
argument path: aesp_config[std-sp]/default_step_config/executor/type
possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

step_configs:#
type: dict
argument path: aesp_config[std-sp]/step_configs

Configuration for each step in the workflow.

gen_struc_step:#
type: dict, optional, default: {'template_config': {'image': None, 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: aesp_config[std-sp]/step_configs/gen_struc_step

Step configuration for structure generation

template_config:#
type: dict, optional, default: {'image': None}
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_config

The configs passed to the PythonOPTemplate.

image:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_config/image

The image to run the step.

timeout:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_config/retry_on_transient_error

The number of retry times if a TransientError is raised.

timeout_as_transient_error:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_config/envs

The environmental variables.

template_slice_config:#
type: dict, optional
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_slice_config

The configs passed to the Slices.

group_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_slice_config/group_size

The number of tasks running on a single node. It is efficient for a large number of short tasks.

pool_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/template_slice_config/pool_size

The number of tasks running at the same time on one node.

continue_on_failed:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/continue_on_failed

If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).

continue_on_num_success:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/continue_on_num_success

Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.

continue_on_success_ratio:#
type: float | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/continue_on_success_ratio

Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.

parallelism:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/parallelism

The parallelism for the step

executor:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/executor

The executor of the step.

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: dispatcher
argument path: aesp_config[std-sp]/step_configs/gen_struc_step/executor/type
possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

scheduler_step:#
type: dict, optional, default: {'template_config': {'image': None, 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: aesp_config[std-sp]/step_configs/scheduler_step

Step configuration for scheduler

template_config:#
type: dict, optional, default: {'image': None}
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_config

The configs passed to the PythonOPTemplate.

image:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_config/image

The image to run the step.

timeout:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_config/retry_on_transient_error

The number of retry times if a TransientError is raised.

timeout_as_transient_error:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_config/envs

The environmental variables.

template_slice_config:#
type: dict, optional
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_slice_config

The configs passed to the Slices.

group_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_slice_config/group_size

The number of tasks running on a single node. It is efficient for a large number of short tasks.

pool_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/template_slice_config/pool_size

The number of tasks running at the same time on one node.

continue_on_failed:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/scheduler_step/continue_on_failed

If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).

continue_on_num_success:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/continue_on_num_success

Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.

continue_on_success_ratio:#
type: float | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/continue_on_success_ratio

Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.

parallelism:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/parallelism

The parallelism for the step

executor:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/scheduler_step/executor

The executor of the step.

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: dispatcher
argument path: aesp_config[std-sp]/step_configs/scheduler_step/executor/type
possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

prep_calc_step:#
type: dict, optional, default: {'template_config': {'image': None, 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: aesp_config[std-sp]/step_configs/prep_calc_step

Step configuration for prepare calcalation

template_config:#
type: dict, optional, default: {'image': None}
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_config

The configs passed to the PythonOPTemplate.

image:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_config/image

The image to run the step.

timeout:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_config/retry_on_transient_error

The number of retry times if a TransientError is raised.

timeout_as_transient_error:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_config/envs

The environmental variables.

template_slice_config:#
type: dict, optional
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_slice_config

The configs passed to the Slices.

group_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_slice_config/group_size

The number of tasks running on a single node. It is efficient for a large number of short tasks.

pool_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/template_slice_config/pool_size

The number of tasks running at the same time on one node.

continue_on_failed:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/continue_on_failed

If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).

continue_on_num_success:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/continue_on_num_success

Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.

continue_on_success_ratio:#
type: float | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/continue_on_success_ratio

Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.

parallelism:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/parallelism

The parallelism for the step

executor:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/executor

The executor of the step.

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: dispatcher
argument path: aesp_config[std-sp]/step_configs/prep_calc_step/executor/type
possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

run_calc_step:#
type: dict, optional, default: {'template_config': {'image': None, 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: aesp_config[std-sp]/step_configs/run_calc_step

Step configuration for run calcalation

template_config:#
type: dict, optional, default: {'image': None}
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_config

The configs passed to the PythonOPTemplate.

image:#
type: str | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_config/image

The image to run the step.

timeout:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_config/retry_on_transient_error

The number of retry times if a TransientError is raised.

timeout_as_transient_error:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_config/envs

The environmental variables.

template_slice_config:#
type: dict, optional
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_slice_config

The configs passed to the Slices.

group_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_slice_config/group_size

The number of tasks running on a single node. It is efficient for a large number of short tasks.

pool_size:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/template_slice_config/pool_size

The number of tasks running at the same time on one node.

continue_on_failed:#
type: bool, optional, default: False
argument path: aesp_config[std-sp]/step_configs/run_calc_step/continue_on_failed

If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).

continue_on_num_success:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/continue_on_num_success

Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.

continue_on_success_ratio:#
type: float | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/continue_on_success_ratio

Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.

parallelism:#
type: int | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/parallelism

The parallelism for the step

executor:#
type: dict | NoneType, optional, default: None
argument path: aesp_config[std-sp]/step_configs/run_calc_step/executor

The executor of the step.

Depending on the value of type, different sub args are accepted.

type:#
type: str (flag key), default: dispatcher
argument path: aesp_config[std-sp]/step_configs/run_calc_step/executor/type
possible choices: dispatcher

The type of the executor.

When type is set to dispatcher: