344 Part B Atoms
22.6 Numerical Approximation of Central Field Dirac Equations
The main drive for understanding methods of numerical
approximation of solutions of Dirac’s equation comes
from their application to many-electron systems. Ap-
proximate wave functions for atomic or molecular states
are usually constructed from products of one-electron
orbitals, and their determination exploits knowledge
gained from the treatment of one-electron problems.
Whilst the numerical methods described here are strictly
one-electron in character, extension to many-electron
problems is relatively straightforward.
22.6.1 Finite Differences
The numerical approximation of eigensolutions of the
first order system of differential equations (22.101)
E
P
Eκ
(r)
Q
Eκ
(r)
=
mc
2
+V(r) −c
$
d
dr
−
κ
r
%
c
$
d
dr
+
κ
r
%
−mc
2
+V(r)
P
Eκ
(r)
Q
Eκ
(r)
(22.127)
can be achieved by more or less standard finite differ-
ence methods given in texts such as [22.25]. For states
in either continuum, E > mc
2
or E < −mc
2
, the calcu-
lation is completely specified as an initial value problem
for a prescribed value of E starting from power series
solutions in the neighborhood of r = 0. Solutions of this
sort exist for all values of (complex) E except at the
bound eigensolutions in the gap −mc
2
< E < mc
2
.For
bound states, the calculation becomes that of a two-point
boundary value problem in which the eigenvalue E has
to be determined iteratively along with the numerical
solution. We concentrate on the latter, which is more
involved.
It is convenient to write
nκ
= E
nκ
−mc
2
, (22.128)
so that approaches the nonrelativistic eigenvalue in the
limit c →∞. For the one-electron problem, (22.101)
can be written in the general form
J
du
ds
+
1
c
dr
ds
r +W(s)
u(s) = χ(s)
dr
ds
,
(22.129)
where u(s) and χ(s) are two-component vectors, such
that
u(s) =
P(s)
Q(s)
, J =
01
−10
,
W(s) =
−rV(r) −cκ
−cκ 2rc
2
−rV(r)
,
and r(s) is a smooth differentiable function of a new
independent variable s. This facilitates the use of a uni-
form grid for s mapping onto a suitable nonuniform grid
for r. Common choices are
r
n
=r
0
e
s
n
, s
n
= nh, n = 0, 1, 2,... ,N ,
for suitable values of the parameters r
0
and h,and
Ar
n
+log
1 +
r
n
r
0
= s
n
, n = 0, 1, 2,... ,N ,
where A is a constant, chosen so that the spacing
in r
n
increases exponentially for small values of n and
approaches a constant for large values of n. The expo-
nentially increasing spacing is appropriate for tightly
bound solutions, but a nearly linear spacing is advisable
to ensure numerical stability in the tails of extended and
continuum solutions.
The most convenient numerical algorithm involves
double shooting from s
0
= 0ands
N
= Nh towards an
intermediate join point s = Jh, adjusting until the
trial solutions have the right number of nodes and have
left- and right-limits at s = Jh which agree to a pre-set
tolerance (commonly about 1 part in 10
8
).
The deferred correction method [22.26, 27] allows
the precision of the numerical approximation to be
improved as the iteration converges. Consider the sim-
plest implicit linear difference scheme for the first order
system
dy
ds
= F
[
y(s), s
]
,
based on the trapezoidal rule of quadrature, is
z
j+1
−z
j
=
1
2
h(F
j+1
+ F
j
), (22.130)
which has a local truncation error O(h
2
). The preci-
sion can be improved, at the expense of increasing the
computational cost per iterative cycle, by adding higher
order difference terms to the right-hand side in (22.130).
Use of the trial solution from the previous cycle leaves
the stability properties of (22.130) are unaltered, but the
converged solution has much higher accuracy.
To apply this to the Dirac system, write f(s) = dr/ ds
and
A
±
j
= J±
h
2c
f(s
j
)W(s
j
).
Part B 22.6