IMLCV.base.MdEngine

MD engine class peforms MD simulations in a given NVT/NPT ensemble.

Classes

StaticMdInfo
TrajectoryInfo	Base class for dataclasses that should act like a JAX pytree node.
MDEngine	Base class for MD engine.

Module Contents

class IMLCV.base.MdEngine.StaticMdInfo

_attr = ['timestep', 'r_cut', 'timecon_thermo', 'T', 'P', 'timecon_baro', 'write_step', 'equilibration',...

_arr = ['atomic_numbers']

timestep: float

T: float

timecon_thermo: float

atomic_numbers: jax.Array

r_cut: float | None = None

P: float | None = None

timecon_baro: float | None = None

write_step: int = 100

equilibration: float | None = None

screen_log: int = 1000

invalid: bool = False

max_grad: float | None = 0.04031059429903557

property masses

property thermostat

property barostat

property neighbour_list_info: IMLCV.base.CV.NeighbourListInfo

__post_init__()

_save(hf: h5py.File)

save(filename: str | pathlib.Path)

static _load(hf: h5py.File) → StaticMdInfo

static load(filename) → StaticMdInfo

class IMLCV.base.MdEngine.TrajectoryInfo(*args, **kwargs)

Bases: IMLCV.base.datastructures.MyPyTreeNode

Base class for dataclasses that should act like a JAX pytree node.

_positions: jax.Array

_cell: jax.Array | None = None

_charges: jax.Array | None = None

_e_pot: jax.Array | None = None

_e_bias: jax.Array | None = None

_w: jax.Array | None = None

_rho: jax.Array | None = None

_cv: jax.Array | None = None

_cv_orig: jax.Array | None = None

_T: jax.Array | None = None

_P: jax.Array | None = None

_err: jax.Array | None = None

_t: jax.Array | None = None

_capacity: int

_size: int

_finished: int

_invalid: int

_items_scal = ['_t', '_e_pot', '_e_bias', '_T', '_P', '_err', '_w', '_rho']

_items_vec = ['_positions', '_cell', '_charges', '_cv', '_cv_orig']

static create(positions: jax.Array, cell: jax.Array | None = None, charges: jax.Array | None = None, e_pot: jax.Array | None = None, e_bias: jax.Array | None = None, cv: jax.Array | None = None, cv_orig: jax.Array | None = None, w: jax.Array | None = None, rho: jax.Array | None = None, T: jax.Array | None = None, P: jax.Array | None = None, err: jax.Array | None = None, t: jax.Array | None = None, capacity: int = -1, size: int = -1, finished=False, invalid=False) → TrajectoryInfo

__getitem__(slices)

gets slice from indices. the output is truncated to the to include only items wihtin _size

_stack(*ti: TrajectoryInfo)

__add__(ti: TrajectoryInfo) → TrajectoryInfo

_expand_capacity(nc=None) → TrajectoryInfo

_shrink_capacity() → TrajectoryInfo

save(filename: str | pathlib.Path)

_save(hf: h5py.File)

static load(filename) → TrajectoryInfo

static _load(hf: h5py.File)

property sp: IMLCV.base.CV.SystemParams

property positions: jax.Array | None

property cell: jax.Array | None

property volume

property charges: jax.Array | None

property e_pot: jax.Array | None

property w: jax.Array | None

property rho: jax.Array | None

property e_bias: jax.Array | None

property cv: jax.Array | None

property cv_orig: jax.Array | None

property T: jax.Array | None

property P: jax.Array | None

property err: jax.Array | None

property t: jax.Array | None

property shape

property CV: IMLCV.base.CV.CV | None

property CV_orig: IMLCV.base.CV.CV | None

class IMLCV.base.MdEngine.MDEngine

Bases: abc.ABC

Base class for MD engine.

bias: IMLCV.base.bias.Bias

energy: IMLCV.base.bias.Energy

sp: IMLCV.base.CV.SystemParams

static_trajectory_info: StaticMdInfo

trajectory_info: TrajectoryInfo | None

trajectory_file: pathlib.Path | None = None

time0: float

step: int = 1

nl: IMLCV.base.CV.NeighbourList | None = None

r_skin = 1.889726133921252

classmethod create(bias: IMLCV.base.bias.Bias, energy: IMLCV.base.bias.Energy, static_trajectory_info: StaticMdInfo, trajectory_info=None, trajectory_file=None, sp: IMLCV.base.CV.SystemParams | None = None, **kwargs) → typing_extensions.Self

update_nl()

save(file)

static load(file, **kwargs) → MDEngine

new_bias(bias: IMLCV.base.bias.Bias, **kwargs) → MDEngine

run(steps)

run the integrator for a given number of steps.

Parameters:

steps – number of MD steps

abstract _run(steps)

get_trajectory() → TrajectoryInfo

save_step(T=None, P=None, t=None, err=None, cv=None, e_bias=None, e_pot=None, gpos_rmsd=None, gpos_bias_rmsd=None, canonicalize=False)

get_energy(sp: IMLCV.base.CV.SystemParams, gpos: bool = False, vtens: bool = False, manual_vir=None) → IMLCV.base.bias.EnergyResult

get_bias(sp: IMLCV.base.CV.SystemParams, gpos: bool = False, vtens: bool = False, shmap: bool = False, use_jac=False, push_jac=False, rel=False, shmap_kwargs=ShmapKwargs.create()) → tuple[IMLCV.base.CV.CV, IMLCV.base.bias.EnergyResult]

__getstate__()

__setstate__(statedict: dict)