Source code for netket.operator._kinetic
# Copyright 2021 The NetKet Authors - All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional, Callable, Union, List
from functools import partial
import numpy as np
import jax
import jax.numpy as jnp
from netket.utils.types import DType, PyTree, Array
from netket.hilbert import AbstractHilbert
from netket.operator import ContinuousOperator
class KineticEnergy(ContinuousOperator):
r"""This is the kinetic energy operator (hbar = 1). The local value is given by:
:math:`E_{kin} = -1/2 ( \sum_i \frac{1}{m_i} (\log(\psi))'^2 + (\log(\psi))'' )`
"""
[docs] def __init__(
self,
hilbert: AbstractHilbert,
mass: Union[float, List[float]],
dtype: Optional[DType] = None,
):
r"""Args:
hilbert: The underlying Hilbert space on which the operator is defined
mass: float if all masses are the same, list indicating the mass of each particle otherwise
dtype: Data type of the matrix elements. Defaults to `np.float64`
"""
self._mass = jnp.asarray(mass, dtype=dtype)
self._is_hermitian = np.allclose(self._mass.imag, 0.0)
super().__init__(hilbert, self._mass.dtype)
@property
def mass(self):
return self._mass
@property
def is_hermitian(self):
return self._is_hermitian
def _expect_kernel(
self, logpsi: Callable, params: PyTree, x: Array, mass: Optional[PyTree]
):
def logpsi_x(x):
return logpsi(params, x)
dlogpsi_x = jax.grad(logpsi_x)
basis = jnp.eye(x.shape[0])
y, f_jvp = jax.linearize(dlogpsi_x, x)
dp_dx2 = jnp.diag(jax.vmap(f_jvp)(basis))
dp_dx = dlogpsi_x(x) ** 2
return -0.5 * jnp.sum(mass * (dp_dx2 + dp_dx), axis=-1)
@partial(jax.vmap, in_axes=(None, None, None, 0, None))
def _expect_kernel_batched(
self, logpsi: Callable, params: PyTree, x: Array, coefficient: Optional[PyTree]
):
return self._expect_kernel(logpsi, params, x, coefficient)
def _pack_arguments(self) -> PyTree:
return 1.0 / self._mass
def __repr__(self):
return f"KineticEnergy(m={self._mass})"