We explore whether the topology of energy landscapes in chemical systems obeys any rules and what these rules are. To answer this and related questions we use several tools: (i) Reduced energy surface and its density of states, (ii) descriptor of structure called fingerprint function, which can be represented as a one-dimensional function or a vector in abstract multidimensional space, (iii) definition of a “distance” between two structures enabling quantification of energy landscapes, (iv) definition of a degree of order of a structure, and (v) definitions of the quasi-entropy quantifying structural diversity. Our approach can be used for rationalizing large databases of crystal structures and for tuning computational algorithms for structure prediction. It enables quantitative and intuitive representations of energy landscapes and reappraisal of some of the traditional chemical notions and rules. Our analysis confirms the expectations that low-energy minima are clustered in compact regions of configuration space (“funnels”) and that chemical systems tend to have very few funnels, sometimes only one. This analysis can be applied to the physical properties of solids, opening new ways of discovering structure-property relations. We quantitatively demonstrate that crystals tend to adopt one of the few simplest structures consistent with their chemistry, providing a thermodynamic justification of Pauling’s fifth rule.

2.
D. J.
Wales
,
M. A.
Miller
, and
T. R.
Walsh
,
Nature (London)
394
,
758
(
1998
).
3.
L.
Angelani
,
G.
Ruocco
, and
M.
Sampoli
,
J. Chem. Phys.
119
,
2120
(
2003
).
4.
P.
Shah
and
C.
Chakravarty
,
Phys. Rev. Lett.
88
,
255501
(
2002
).
5.
S.
Büchner
and
A.
Heuer
,
Phys. Rev. E
60
,
6507
(
1999
).
6.
7.
O. M.
Becker
and
M.
Karplus
,
J. Chem. Phys.
106
,
1495
(
1997
).
8.
P. J.
Steinhardt
,
D. R.
Nelson
, and
M.
Ronchetti
,
Phys. Rev. B
28
,
784
(
1983
).
9.
E. L.
Willighagen
,
R.
Wehrens
,
P.
Verwer
,
R.
de Gelder
, and
L. M. C.
Buydens
,
Acta Crystallogr., Sect. B: Struct. Sci.
61
,
29
(
2005
).
10.
R.
de Gelder
,
IUCR Newsl.
7
,
59
(
2006
).
11.
A. R.
Oganov
,
Y.
Ma
,
C. W.
Glass
, and
M.
Valle
,
Psi-k Newsletter
84
,
142
(
2007
).
12.
J. P. K.
Doye
,
Phys. Rev. Lett.
88
,
238701
(
2002
).
13.
J. P.
Perdew
,
K.
Burke
, and
M.
Ernzerhof
,
Phys. Rev. Lett.
77
,
3865
(
1996
).
14.
At sufficiently large system size, structures corresponding to the second, third, … lowest energy minima will simply be defective versions of the lowest-energy structure.
15.
J. P. K.
Doye
,
M. A.
Miller
, and
D. J.
Wales
,
J. Chem. Phys.
110
,
6896
(
1999
).
16.
M.
Valle
and
A. R.
Oganov
,
Visual analytics science and technology
,
VAST '08, IEEE Symposium
, pp.
11−18
, Oct. 19−24,
2008
.
17.
D. G.
Pettifor
,
Solid State Commun.
51
,
31
(
1984
).
18.
19.
The high-dimensional space is almost empty and the whole concept of neighborhood is meaningless in high dimensions. Randomly generated points in highly dimensional spaces tend to be all at the same distance [the relative contrast (dmaxdmin)/dmin0 as dimensionality tends to infinity] and under small perturbation the nearest point could change into the farthest one. These phenomena are not intrinsic to the high-dimensional space, but depend strongly on the distance function used. In this respect cosine distance is superior to Euclidean distance (or in general, the Minkowski distances with p>1).
20.
An alternative useful definition would be λ=V1/3.
21.
For this, one needs to compute a fingerprint for each ith atom (rather than atomic species) defined by analogy with Eq. (3) as FAiB=Bj{δ(RRij)/[4πRij2(NB/V)Δ]}1
22.
G.
Kresse
and
J.
Furthmüller
,
Comput. Mater. Sci.
6
,
15
(
1996
).
23.
G. V.
Lewis
and
C. R. A.
Catlow
,
J. Phys. C
18
,
1149
(
1985
).
24.
25.
S.
Roy
,
S.
Goedecker
, and
V.
Hellmann
,
Phys. Rev. E
77
,
056707
(
2008
).
26.
A. R.
Oganov
,
C. W.
Glass
, and
S.
Ono
,
Earth Planet. Sci. Lett.
241
,
95
(
2006
).
27.
A. R.
Oganov
and
C. W.
Glass
,
J. Chem. Phys.
124
,
244704
(
2006
).
28.
C. W.
Glass
,
A. R.
Oganov
, and
N.
Hansen
,
Comput. Phys. Commun.
175
,
713
(
2006
).
29.
Of course, such mapping with reduced dimensionality cannot fully preserve distances (for the same reason, 2D maps of the world show distorted distances). However, the topology of the landscape is correctly reproduced.
30.
J. C.
Schon
and
M.
Jansen
,
Angew. Chem., Int. Ed. Engl.
35
,
1286
(
1996
).
31.
C. J.
Pickard
and
R. J.
Needs
,
J. Chem. Phys.
127
,
244503
(
2007
).
32.
L.
Pauling
,
J. Am. Chem. Soc.
51
,
1010
(
1929
).
33.
A. R.
Oganov
and
C. W.
Glass
,
J. Phys.: Condens. Matter
20
,
064210
(
2008
).
34.
G. L. W.
Hart
,
Nature Mater.
6
,
941
(
2007
).
35.
To quantify the simplicity of an alloy structure, Hart (Ref. 34) introduced an index measuring the nonrandomness of the distribution of atoms over sites. His index is valid only for the case of ordering in alloys. Our degree of order [Eqs. (9a) and (9b)] and quasi-entropy [Eq. (13)] provide universal criteria equally valid for ordering in alloys of a given structure, as well as for the comparison of completely different structures.
36.
A. R.
Oganov
,
J.
Chen
,
C.
Gatti
,
Y. -M.
Ma
,
T.
Yu
,
Z.
Liu
,
C. W.
Glass
,
Y. -Z.
Ma
,
O. O.
Kurakevych
, and
V. L.
Solozhenko
,
Nature (London)
457
,
863
(
2009
).
37.
J. S.
van Duijneveldt
and
D.
Frenkel
,
J. Chem. Phys.
96
,
4655
(
1992
).
38.
A.
Parkin
,
G.
Barr
,
W.
Dong
,
C. J.
Gilmore
,
D.
Jayatilaka
,
J. J.
MCKinnon
,
M. A.
Spackman
, and
C. C.
Wilson
,
Cryst. Eng. Comm.
9
,
648
(
2007
).
39.
F.
Camastra
and
A.
Vinciarelli
,
Neural Process. Lett.
14
,
27
(
2001
).
40.
A. O.
Lyakhov
,
A. R.
Oganov
, and
M.
Valle
, “How to predict large and complex crystal structures” (unpublished).
You do not currently have access to this content.