Effective potentials for quasicrystals from ab-initio data
Peter Brommer∗and Franz Gähler
Institut für Theoretische und Angewandte Physik
Universität Stuttgart
November 2, 2018
Abstract
Classical effective potentials are indispensable for any large-scale atomistic sim-
ulations, and the relevance of simulation results crucially depends on the quality of
the potentials used. For complex alloys like quasicrystals, however, realistic effective
potentials are practically inexistent. We report here on our efforts to develop effec-
tive potentials especially for quasicrystalline alloy systems. We use the so-called force
matching method, in which the potential parameters are adapted so as to optimally
reproduce the forces and energies in a set of suitably chosen reference configurations.
These reference data are calculated with ab-initio methods. As a first application,
EAM potentials for decagonal Al-Ni-Co, icosahedral Ca-Cd, and both icosahedral
and decagonal Mg-Zn quasicrystals have been constructed. The influence of the po-
tential range and degree of specialisation on the accuracy and other properties is
discussed and compared.
Keywords: force matching; quasicrystal; effective potential; molecular dynamics; ab
initio
1 Introduction
Large-scale molecular dynamics simulations are possible only with classical effective po-
tentials, which reduce the quantum-mechanical interactions of electrons and nuclei to an
effective interaction between the atom cores. The computational task is thereby greatly
simplified. Whereas ab-initio simulations are limited to a few hundred atoms at most,
classical simulations can be done routinely with multi-million atom systems. For many
purposes, such system sizes are indispensable. For example, fracture studies of quasicrys-
tals require samples with several million atoms at least [1]. Diffusion studies, on the other
∗e-mail: p.brommer@itap.physik.uni-stuttgart.de
hand, can be done with a few thousand atoms (or even less), but require very large simu-
lated times of the order of nanoseconds [2], which also makes them infeasible for ab-initio
simulations.
While physically justified effective potentials have been constructed for many elemen-
tary solids, such potentials are rare for complex intermetallic alloys. For this reason, molec-
ular dynamics simulations of these materials have often been done with simple model po-
tentials, resulting in rather limited reliability and predictability. In order to make progress,
better potentials are needed to accurately simulate complex materials.
The force matching method [3] provides a way to construct physically reasonable po-
tentials also for more complex solids, where a larger variety of local environments has to
be described correctly, and many potential parameters need to be determined. The idea is
to compute forces and energies from first principles for a suitable selection of small refer-
ence systems, and to fit the potential parameters so that they optimally reproduce these
reference data. Hereafter, potentials generated in this way will be referred to as fitted
potentials. Thus, the force matching method allows to make use of the results of ab-initio
simulations also for large-scale classical simulations, thereby bridging the gap between the
sample sizes supported by these two methods.
2 Force Matching
As we intend to construct potentials for complex intermetallic alloys, we have to assume
a functional form which is suitable for metals. A good choice are EAM (Embedded Atom
Method) potentials [4], also known as glue potentials [5]. Such potentials have been used
very successfully for many metals, and are still efficient to compute, even though they
include many-body terms. In contrast, pure pair potentials show a number of deficiencies
when it comes to describe metals [5]. The functional form of EAM potentials is given by
i,j<i
φkikj(rij) +
Uki(ni), with ni =
j 6=i
ρkj(rij), (1)
where φkikj is a pair potential term depending on the two atom types kl. Uki describes
the embedding term that represents an additional energy for each atom. This energy is a
function of a local density ni determined by contributions ρkj of the neighbouring atoms.
It is tempting to view this as embedding each atom into the electron sea provided by
its neighbours. Such an interpretation is not really meaningful, however. The potential
(1) is invariant under a family of “gauge” transformations [5], by which one can move
contributions from the embedding term to the pair term, and vice versa, so that it makes
little sense to give any of them an individual physical interpretation.
In order to allow for maximal flexibility, and to avoid any bias, the potential functions
in (1) are represented by tabulated values and spline interpolation, the tabulated values
acting as potential parameters. This makes it unnecessary to guess the right analytic form
beforehand. The sampling points can be chosen freely, which is useful for functions which
vary rapidly in one region, but only slowly in another region.
The forces and energies in the reference structures are computed with VASP, the Vi-
enna Ab-Initio Simulation Package [6, 7], using the Projector Augmented Wave (PAW)
method [8, 9]. Like all plane wave based ab-initio codes, VASP requires periodic bound-
ary conditions. For quasicrystals, this means that periodic approximants have to be used
as reference structures. As ab-initio methods are limited to a few hundred atoms, those
approximants must be rather small. For the systems studied so far, this was not a major
problem, as the relevant local environments in the quasicrystal all occur also in reasonably
small approximants. Icosahedral quasicrystals with F-type lattice may be more problem-
atic in this respect. For these, small approximants are rare, and the force matching method
requires a sufficient variety of reference structures.
Given the reference data (forces, energies, and stresses in the reference structures),
the potential parameters (in our case: up to about 120 EAM potential sampling points
for spline interpolation) then are optimised in a non-linear least square fit, so that the
fitted potential reproduces the reference data as well as possible. The target function to be
minimised is a weighted sum of the squared deviations between the reference data, denoted
by the subscript 0 below, and the corresponding data computed from the fitted effective
potential. It is of the form
Z = ZF + ZC, with (2)
α=x,y,z
(fjα − f0,jα)
f 20,j + εj
, and ZC =
(Ak − A0,k)
A20,k + εk
, (3)
where ZF represents the contributions of the forces f j, and ZC those of some collective
quantities like total stresses and energies, but also additional constraintsAk on the potential
one would like to impose. The denominators of the fractions ensure that the target function
measures the relative deviations from the reference data, except for really tiny quantities,
where the εl prevent extremely small denominators. The Wl are the weights of the different
terms. It proves useful for the fitting to give the total stresses and the cohesion energies a
higher weight, although in principle they should be reproduced correctly already from the
forces.
We developed a programme named potfit, which optimises the potential parameters to
a set of reference data. It consists of two largely independent parts. The first part imple-
ments a particular parametrised potential model. It takes a list of potential parameters
and computes from it the target function, i.e., the deviations of the forces, energies, and
stresses from the reference data. Wrapped around this part is a second, potential indepen-
dent part, which implements a least square minimisation module, using a combination of a
deterministic conjugate gradient algorithm [10] and a stochastic simulated annealing algo-
rithm [11]. This part knows nothing about the details of the potential, and only deals with
a list of potential parameters. The programme architecture thus makes it easy to replace
the potential dependent part by a different one, e.g., one which implements a different
potential model, or a different way to parametrise it.
3 Results and Applications
We generated several fitted potentials for decagonal Al-Ni-Co and icosahedral Ca-Cd qua-
sicrystals, as well as Mg-Zn potentials suitable for both icosahedral and decagonal phases.
In a first step, classical molecular dynamics simulations with simple model potentials were
used to create reference configurations from small approximants (80–250 atoms). These in-
cluded samples at different temperatures, but also samples which were scaled and strained
in different ways. The approximants were carefully selected, so that all relevant local envi-
ronments are represented. For those reference structures, the forces, stresses and energies
were computed with ab-initio methods, and a first version of the fitted effective potential
given by sampling points with cubic spline interpolation was fitted to the reference data.
In a second step, molecular dynamics simulations with the newly determined potential
were used to create new reference structures, which are better representatives of the struc-
tures actually appearing in that system. The new reference structures complemented and
partially replaced the previous ones, and the fitting procedure was repeated. This second
iteration resulted in a significantly better fit to the reference data. In order to test the
transferability of the fitted potentials, further samples similar to the reference structures
were created, and their ab-initio forces and energies were compared to those determined
by the classical potentials. The deviations were of the same order as the deviations found
in the potential fit, which shows that the fitted potentials transfer well to similar struc-
tures. For Al-Ni-Co, a force-matched potential is displayed in figure 1. Fitted potentials
for Ca-Cd and Mg-Zn are not displayed here for space constraints, but are available from
the authors.
The potentials developed for decagonal Al-Ni-Co quasicrystals are intended to be used
in high-temperature diffusion simulations [2]. It is therefore important that they describe
high temperature states well, which is achieved by selecting the reference structures ac-
cordingly. By using high temperature reference structures, the fitted potential is especially
trained to such situations. As part of the potential validation, the melting temperature was
determined by slowly heating the sample at constant pressure, and the elastic constants of
decagonal Al-Ni-Co were determined. We actually have constructed two potential variants:
Variant A gives excellent values for the elastic constants (Table 1), but produces a melting
temperature which is somewhat too high. Conversely, variant B shows larger deviations
in the elastic constants, but gives a very reasonable value of the melting temperature of
about 1300 K. It is a general experience that with an effective potential it is often not
possible to reproduce all desired quantities equally well at the same time.
In complex intermetallic systems there are many competing candidates for the ground
state structure. This is the case also for complex crystalline systems. In principle, the
ground state of these can be determined directly by ab-initio simulations, but for large
unit cells this is extremely time-consuming, or even impossible. Classical potentials can be
used to select the most promising candidates, and to pre-relax them, so that the time for
ab-initio relaxation can be dramatically reduced. Potentials used for this purpose must be
able to discriminate energy differences of the order of a meV/atom. This has been largely
achieved with fitted potentials for the Mg-Zn and Ca-Cd systems, by using mainly near
 1  2  3  4  5  6  7  8
distance [ Å ]
Al−Al
Al−Co
Al−Ni
 1  2  3  4  5  6  7  8
distance [ Å ]
Co−Co
Co−Ni
Ni−Ni
−0.10
−0.05
 1  2  3  4  5  6  7  8tr
distance [ Å ]
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
(total) local "electron density"
Figure 1: Potential functions for decagonal Al-Ni-Co
ground state structures as reference structures. Also, for this application it is important to
choose a small εj in equation (3), so that small forces are also reproduced accurately. The so
constructed Ca-Cd potentials have been used successfully for structure optimisations [13].
4 Discussion and Conclusion
The selection of the reference structures used for the potential fit largely determines the
capabilities of the resulting potential. For a precise determination of the ground state,
low temperature structures should be dominant in the reference structures, and it must be
assured that even small forces and energy differences are reproduced accurately. For high
temperature simulations, on the other hand, typical high temperature structures must be
predominant in the reference structures. This opens the possibility to design specialised
potentials for certain purposes by a suitable selection of reference structures. It should
be kept in mind, however, that a fitted potential can only deal with situations it has
been trained to. For instance, one should not expect a fitted potential to handle surfaces
correctly, if it was trained only with bulk systems. Clearly, there is always a trade-off
Table 1: Elastic constants of decagonal Al-Ni-Co
[GPa] c11 c33 c44 c66
a c12 c13
Exp. [12] 234 232 70 88 57 67
Pot. A 230 231 55 70 91 91
Pot. B 197 187 49 58 86 84
aIn decagonal QC: c66 = 12 (c11 − c12)
between the transferability and the accuracy of a fitted potential. A potential can be
made more versatile by training it with many different kinds of structures, but the more
versatile it becomes, the less accurate it will be on average. Conversely, very accurate fitted
potentials will probably have limited transferability.
For practical applications, the range of a potential is also an important issue, as it
enters in the third power in the computational effort of molecular dynamics. Allowing for
a larger potential range results in greater flexibility of the potential, which might improve
its accuracy, but this comes at the price of a slower simulation. We therefore need a
compromise between speed and accuracy. The potential range should only be increased as
long as this can improve the potential quality. In a first step, our fitted potentials were
constructed with a fairly generous range of about 7Å. It turned out, however, that especially
the transfer function ρi did not make effective use of this range, and was essentially zero
beyond 5Å. In a second fit we therefore restricted the range of ρi to 5Å, without significant
loss of accuracy. This is one of the advantages of using tabulated functions: The system
itself chooses the optimal functions, including the optimal range.
Force Matching has proven to be a versatile method to construct physically reasonable,
accurate effective potentials even for structures as complicated as quasicrystals and their
approximants. Our potfit programme makes it easy to apply this method to different sys-
tems, and is also easy to adapt for the support of further potential models. The potentials
constructed so far have successfully been used in high temperature diffusion simulations
of decagonal Al-Ni-Co [2], and in structure optimisation of approximants in the Zn-Mg
and Ca-Cd systems. Further fruitful applications of the fitted potentials can certainly be
expected, and we hope to apply our methods also to other complex alloy systems, where
reliable potentials are still lacking.
Acknowledgement
This work was funded by the Deutsche Forschungsgemeinschaft through Sonderforschungs-
bereich 382. Special thanks go to Marek Mihalkovič for supplying approximants and feed-
back in the Ca-Cd and Mg-Zn systems, and to Hans-Rainer Trebin for supervising the
thesis work of the first author.
References
[1] F. Rösch, Ch. Rudhart, J. Roth, H.-R. Trebin, and P. Gumbsch, Phys. Rev. B 72,
014128 (2005).
[2] S. Hocker, F. Gähler, and P. Brommer, Phil. Mag. 86, 1051 (2006).
[3] F. Ercolessi and J. B. Adams, Europhys. Lett. 26, 583 (1994).
[4] M. S. Daw and M. I. Baskes, Phys. Rev. B 29, 6443 (1984).
[5] F. Ercolessi, M. Parrinello, and E. Tosatti, Phil. Mag. A 58, 213 (1988).
[6] G. Kresse and J. Hafner, Phys. Rev. B 47, 558 (1993).
[7] G. Kresse and J. Furthmüller, Phys. Rev. B 54, 11169 (1996).
[8] P. E. Blöchl, Phys. Rev. B 50, 17953 (1994).
[9] G. Kresse and D. Joubert, Phys. Rev. B 59, 1758 (1999).
[10] M. J. D. Powell, Comp. J. 7, 303 (1965).
[11] A. Corana, M. Marchesi, C. Martini, and S. Ridella, ACM Trans. Math. Soft. 13, 262
(1987).
[12] M. A. Chernikov, H. R. Ott, A. Bianchi, A. Migliori, and T. W. Darling, Phys. Rev.
Lett. 80, 321 (1998).
[13] M. Mihalkovič and M. Widom, Phil. Mag. 86, 519 (2006).
	Introduction
	Force Matching
	Results and Applications
	Discussion and Conclusion

  Classical effective potentials are indispensable for any large-scale
atomistic simulations, and the relevance of simulation results crucially
depends on the quality of the potentials used. For complex alloys like
quasicrystals, however, realistic effective potentials are practically
inexistent. We report here on our efforts to develop effective potentials
especially for quasicrystalline alloy systems. We use the so-called force
matching method, in which the potential parameters are adapted so as to
optimally reproduce the forces and energies in a set of suitably chosen
reference configurations. These reference data are calculated with ab-initio
methods. As a first application, EAM potentials for decagonal Al-Ni-Co,
icosahedral Ca-Cd, and both icosahedral and decagonal Mg-Zn quasicrystals have
been constructed. The influence of the potential range and degree of
specialisation on the accuracy and other properties is discussed and compared.

Introduction
Large-scale molecular dynamics simulations are possible only with classical effective po-
tentials, which reduce the quantum-mechanical interactions of electrons and nuclei to an
effective interaction between the atom cores. The computational task is thereby greatly
simplified. Whereas ab-initio simulations are limited to a few hundred atoms at most,
classical simulations can be done routinely with multi-million atom systems. For many
purposes, such system sizes are indispensable. For example, fracture studies of quasicrys-
tals require samples with several million atoms at least [1]. Diffusion studies, on the other
∗e-mail: p.brommer@itap.physik.uni-stuttgart.de
hand, can be done with a few thousand atoms (or even less), but require very large simu-
lated times of the order of nanoseconds [2], which also makes them infeasible for ab-initio
simulations.
While physically justified effective potentials have been constructed for many elemen-
tary solids, such potentials are rare for complex intermetallic alloys. For this reason, molec-
ular dynamics simulations of these materials have often been done with simple model po-
tentials, resulting in rather limited reliability and predictability. In order to make progress,
better potentials are needed to accurately simulate complex materials.
The force matching method [3] provides a way to construct physically reasonable po-
tentials also for more complex solids, where a larger variety of local environments has to
be described correctly, and many potential parameters need to be determined. The idea is
to compute forces and energies from first principles for a suitable selection of small refer-
ence systems, and to fit the potential parameters so that they optimally reproduce these
reference data. Hereafter, potentials generated in this way will be referred to as fitted
potentials. Thus, the force matching method allows to make use of the results of ab-initio
simulations also for large-scale classical simulations, thereby bridging the gap between the
sample sizes supported by these two methods.
2 Force Matching
As we intend to construct potentials for complex intermetallic alloys, we have to assume
a functional form which is suitable for metals. A good choice are EAM (Embedded Atom
Method) potentials [4], also known as glue potentials [5]. Such potentials have been used
very successfully for many metals, and are still efficient to compute, even though they
include many-body terms. In contrast, pure pair potentials show a number of deficiencies
when it comes to describe metals [5]. The functional form of EAM potentials is given by
i,j<i
φkikj(rij) +
Uki(ni), with ni =
j 6=i
ρkj(rij), (1)
where φkikj is a pair potential term depending on the two atom types kl. Uki describes
the embedding term that represents an additional energy for each atom. This energy is a
function of a local density ni determined by contributions ρkj of the neighbouring atoms.
It is tempting to view this as embedding each atom into the electron sea provided by
its neighbours. Such an interpretation is not really meaningful, however. The potential
(1) is invariant under a family of “gauge” transformations [5], by which one can move
contributions from the embedding term to the pair term, and vice versa, so that it makes
little sense to give any of them an individual physical interpretation.
In order to allow for maximal flexibility, and to avoid any bias, the potential functions
in (1) are represented by tabulated values and spline interpolation, the tabulated values
acting as potential parameters. This makes it unnecessary to guess the right analytic form
beforehand. The sampling points can be chosen freely, which is useful for functions which
vary rapidly in one region, but only slowly in another region.
The forces and energies in the reference structures are computed with VASP, the Vi-
enna Ab-Initio Simulation Package [6, 7], using the Projector Augmented Wave (PAW)
method [8, 9]. Like all plane wave based ab-initio codes, VASP requires periodic bound-
ary conditions. For quasicrystals, this means that periodic approximants have to be used
as reference structures. As ab-initio methods are limited to a few hundred atoms, those
approximants must be rather small. For the systems studied so far, this was not a major
problem, as the relevant local environments in the quasicrystal all occur also in reasonably
small approximants. Icosahedral quasicrystals with F-type lattice may be more problem-
atic in this respect. For these, small approximants are rare, and the force matching method
requires a sufficient variety of reference structures.
Given the reference data (forces, energies, and stresses in the reference structures),
the potential parameters (in our case: up to about 120 EAM potential sampling points
for spline interpolation) then are optimised in a non-linear least square fit, so that the
fitted potential reproduces the reference data as well as possible. The target function to be
minimised is a weighted sum of the squared deviations between the reference data, denoted
by the subscript 0 below, and the corresponding data computed from the fitted effective
potential. It is of the form
Z = ZF + ZC, with (2)
α=x,y,z
(fjα − f0,jα)
f 20,j + εj
, and ZC =
(Ak − A0,k)
A20,k + εk
, (3)
where ZF represents the contributions of the forces f j, and ZC those of some collective
quantities like total stresses and energies, but also additional constraintsAk on the potential
one would like to impose. The denominators of the fractions ensure that the target function
measures the relative deviations from the reference data, except for really tiny quantities,
where the εl prevent extremely small denominators. The Wl are the weights of the different
terms. It proves useful for the fitting to give the total stresses and the cohesion energies a
higher weight, although in principle they should be reproduced correctly already from the
forces.
We developed a programme named potfit, which optimises the potential parameters to
a set of reference data. It consists of two largely independent parts. The first part imple-
ments a particular parametrised potential model. It takes a list of potential parameters
and computes from it the target function, i.e., the deviations of the forces, energies, and
stresses from the reference data. Wrapped around this part is a second, potential indepen-
dent part, which implements a least square minimisation module, using a combination of a
deterministic conjugate gradient algorithm [10] and a stochastic simulated annealing algo-
rithm [11]. This part knows nothing about the details of the potential, and only deals with
a list of potential parameters. The programme architecture thus makes it easy to replace
the potential dependent part by a different one, e.g., one which implements a different
potential model, or a different way to parametrise it.
3 Results and Applications
We generated several fitted potentials for decagonal Al-Ni-Co and icosahedral Ca-Cd qua-
sicrystals, as well as Mg-Zn potentials suitable for both icosahedral and decagonal phases.
In a first step, classical molecular dynamics simulations with simple model potentials were
used to create reference configurations from small approximants (80–250 atoms). These in-
cluded samples at different temperatures, but also samples which were scaled and strained
in different ways. The approximants were carefully selected, so that all relevant local envi-
ronments are represented. For those reference structures, the forces, stresses and energies
were computed with ab-initio methods, and a first version of the fitted effective potential
given by sampling points with cubic spline interpolation was fitted to the reference data.
In a second step, molecular dynamics simulations with the newly determined potential
were used to create new reference structures, which are better representatives of the struc-
tures actually appearing in that system. The new reference structures complemented and
partially replaced the previous ones, and the fitting procedure was repeated. This second
iteration resulted in a significantly better fit to the reference data. In order to test the
transferability of the fitted potentials, further samples similar to the reference structures
were created, and their ab-initio forces and energies were compared to those determined
by the classical potentials. The deviations were of the same order as the deviations found
in the potential fit, which shows that the fitted potentials transfer well to similar struc-
tures. For Al-Ni-Co, a force-matched potential is displayed in figure 1. Fitted potentials
for Ca-Cd and Mg-Zn are not displayed here for space constraints, but are available from
the authors.
The potentials developed for decagonal Al-Ni-Co quasicrystals are intended to be used
in high-temperature diffusion simulations [2]. It is therefore important that they describe
high temperature states well, which is achieved by selecting the reference structures ac-
cordingly. By using high temperature reference structures, the fitted potential is especially
trained to such situations. As part of the potential validation, the melting temperature was
determined by slowly heating the sample at constant pressure, and the elastic constants of
decagonal Al-Ni-Co were determined. We actually have constructed two potential variants:
Variant A gives excellent values for the elastic constants (Table 1), but produces a melting
temperature which is somewhat too high. Conversely, variant B shows larger deviations
in the elastic constants, but gives a very reasonable value of the melting temperature of
about 1300 K. It is a general experience that with an effective potential it is often not
possible to reproduce all desired quantities equally well at the same time.
In complex intermetallic systems there are many competing candidates for the ground
state structure. This is the case also for complex crystalline systems. In principle, the
ground state of these can be determined directly by ab-initio simulations, but for large
unit cells this is extremely time-consuming, or even impossible. Classical potentials can be
used to select the most promising candidates, and to pre-relax them, so that the time for
ab-initio relaxation can be dramatically reduced. Potentials used for this purpose must be
able to discriminate energy differences of the order of a meV/atom. This has been largely
achieved with fitted potentials for the Mg-Zn and Ca-Cd systems, by using mainly near
 1  2  3  4  5  6  7  8
distance [ Å ]
Al−Al
Al−Co
Al−Ni
 1  2  3  4  5  6  7  8
distance [ Å ]
Co−Co
Co−Ni
Ni−Ni
−0.10
−0.05
 1  2  3  4  5  6  7  8tr
distance [ Å ]
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
(total) local "electron density"
Figure 1: Potential functions for decagonal Al-Ni-Co
ground state structures as reference structures. Also, for this application it is important to
choose a small εj in equation (3), so that small forces are also reproduced accurately. The so
constructed Ca-Cd potentials have been used successfully for structure optimisations [13].
4 Discussion and Conclusion
The selection of the reference structures used for the potential fit largely determines the
capabilities of the resulting potential. For a precise determination of the ground state,
low temperature structures should be dominant in the reference structures, and it must be
assured that even small forces and energy differences are reproduced accurately. For high
temperature simulations, on the other hand, typical high temperature structures must be
predominant in the reference structures. This opens the possibility to design specialised
potentials for certain purposes by a suitable selection of reference structures. It should
be kept in mind, however, that a fitted potential can only deal with situations it has
been trained to. For instance, one should not expect a fitted potential to handle surfaces
correctly, if it was trained only with bulk systems. Clearly, there is always a trade-off
Table 1: Elastic constants of decagonal Al-Ni-Co
[GPa] c11 c33 c44 c66
a c12 c13
Exp. [12] 234 232 70 88 57 67
Pot. A 230 231 55 70 91 91
Pot. B 197 187 49 58 86 84
aIn decagonal QC: c66 = 12 (c11 − c12)
between the transferability and the accuracy of a fitted potential. A potential can be
made more versatile by training it with many different kinds of structures, but the more
versatile it becomes, the less accurate it will be on average. Conversely, very accurate fitted
potentials will probably have limited transferability.
For practical applications, the range of a potential is also an important issue, as it
enters in the third power in the computational effort of molecular dynamics. Allowing for
a larger potential range results in greater flexibility of the potential, which might improve
its accuracy, but this comes at the price of a slower simulation. We therefore need a
compromise between speed and accuracy. The potential range should only be increased as
long as this can improve the potential quality. In a first step, our fitted potentials were
constructed with a fairly generous range of about 7Å. It turned out, however, that especially
the transfer function ρi did not make effective use of this range, and was essentially zero
beyond 5Å. In a second fit we therefore restricted the range of ρi to 5Å, without significant
loss of accuracy. This is one of the advantages of using tabulated functions: The system
itself chooses the optimal functions, including the optimal range.
Force Matching has proven to be a versatile method to construct physically reasonable,
accurate effective potentials even for structures as complicated as quasicrystals and their
approximants. Our potfit programme makes it easy to apply this method to different sys-
tems, and is also easy to adapt for the support of further potential models. The potentials
constructed so far have successfully been used in high temperature diffusion simulations
of decagonal Al-Ni-Co [2], and in structure optimisation of approximants in the Zn-Mg
and Ca-Cd systems. Further fruitful applications of the fitted potentials can certainly be
expected, and we hope to apply our methods also to other complex alloy systems, where
reliable potentials are still lacking.
Acknowledgement
This work was funded by the Deutsche Forschungsgemeinschaft through Sonderforschungs-
bereich 382. Special thanks go to Marek Mihalkovič for supplying approximants and feed-
back in the Ca-Cd and Mg-Zn systems, and to Hans-Rainer Trebin for supervising the
thesis work of the first author.
References
[1] F. Rösch, Ch. Rudhart, J. Roth, H.-R. Trebin, and P. Gumbsch, Phys. Rev. B 72,
014128 (2005).
[2] S. Hocker, F. Gähler, and P. Brommer, Phil. Mag. 86, 1051 (2006).
[3] F. Ercolessi and J. B. Adams, Europhys. Lett. 26, 583 (1994).
[4] M. S. Daw and M. I. Baskes, Phys. Rev. B 29, 6443 (1984).
[5] F. Ercolessi, M. Parrinello, and E. Tosatti, Phil. Mag. A 58, 213 (1988).
[6] G. Kresse and J. Hafner, Phys. Rev. B 47, 558 (1993).
[7] G. Kresse and J. Furthmüller, Phys. Rev. B 54, 11169 (1996).
[8] P. E. Blöchl, Phys. Rev. B 50, 17953 (1994).
[9] G. Kresse and D. Joubert, Phys. Rev. B 59, 1758 (1999).
[10] M. J. D. Powell, Comp. J. 7, 303 (1965).
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	Introduction
	Force Matching
	Results and Applications
	Discussion and Conclusion
