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This package provides Julia bindings to the libxc library for common exchange-correlation functionals in density-functional theory. At least Julia 1.8 is required.
Install the library from Julia as usual:
import Pkg
Pkg.add("Libxc")and then for example:
using Libxc
rho = [0.1, 0.2, 0.3, 0.4, 0.5]
sigma = [0.2, 0.3, 0.4, 0.5, 0.6]
# LDA exchange
lda_x = Functional(:lda_x)
result = evaluate(lda_x, rho=rho)
@show result
# result = (vrho = [-0.457078 -0.575882 -0.659220 -0.725566 -0.781592],
# zk = [-0.342808, -0.43191, -0.49441, -0.544174, -0.586194])
# GGA exchange
gga_x = Functional(:gga_x_pbe, n_spin=1)
result = evaluate(gga_x, rho=rho, sigma=sigma, derivative=0)
@show result
# result = (zk = [-0.452597, -0.478877, -0.520674, -0.561427, -0.598661],)Recently GPU support has been added. Whenever evaluate is called
with CuArrays, the computation will automatically be done with the CUDA
version of libxc. Currently only CUDA 11 is supported,
so you need to enforce using CUDA 11 explicitly:
using CUDA
CUDA.set_runtime_version!(v"11.8")Full support for evaluating LDA, GGA and meta-GGA functionals on CPUs as shown above.
Hybrid or range-separated hybrids and VV10-type functionals
export parameters required in the host programs as properties of the Functional
Julia object. For example
b3lyp = Functional(:hyb_gga_xc_b3lyp)
@show b3lyp.exx_coefficient
# b3lyp.exx_coefficient = 0.2See also the xc-info.jl example (modelled after the
xc-info executable shipped with libxc).
Some advanced Libxc features (custom functional combinations, setting external
parameters etc.) are not yet supported in the Julia wrapper. If you need those
you can, however, talk to libxc directly using the low-level C-like interface,
see the file src/gen/api.jl.
This file is automatically generated from the libxc source code and
offers all functions of the public interface as ccall wrappers.