Accelerating materials property predictions using machine learning

Accelerating materials property
predictions using machine learning
Ghanshyam Pilania1
, Chenchen Wang1
, Xun Jiang2
, Sanguthevar Rajasekaran3
& Ramamurthy Ramprasad1
1
Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Storrs, Connecticut 06269,
2
Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, Connecticut 06269, 3
Department of Computer
Science and Engineering, University of Connecticut, 371 Fairfield Road, Storrs, Connecticut 06269.
The materials discovery process can be significantly expedited and simplified if we can learn effectively from
available knowledge and data. In the present contribution, we show that efficient and accurate prediction of
a diverse set of properties of material systems is possible by employing machine (or statistical) learning
methods trained on quantum mechanical computations in combination with the notions of chemical
similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us
to discover decision rules that establish a mapping between easily accessible attributes of a system and its
properties. It is shown that fingerprints based on either chemo-structural (compositional and
configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet
accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically
explore and mine vast chemical spaces, and can significantly accelerate the discovery of new
application-specific materials.
O
wing to the staggering compositional and configurational degrees of freedom possible in materials, it is
fair to assume that the chemical space of even a restricted subclass of materials (say, involving just two
elements) is far from being exhausted, and an enormous number of new materials with useful properties
are yet to be discovered. Given this formidable chemical landscape, a fundamental bottleneck to an efficient
materials discovery process is the lack of suitable methods to rapidly and accurately predict the properties of a vast
array (within a subclass) of new yet-to-be-synthesized materials. The standard approaches adopted thus far
involve either expensive and lengthy Edisonian synthesis-testing experimental cycles, or laborious and time-
intensive computations, performed on a case-by-case manner. Moreover, neither of these approaches is able to
readily unearth Hume-Rothery-like ‘‘hidden’’ semi-empirical rules that govern materials behavior.
The present contribution, aimed at materials property predictions, falls under a radically different paradigm1,2
,
namely, machine (or statistical) learning—a topic central to network theory3
, cognitive game theory4,5
, pattern
recognition6–8
, artificial intelligence9,10
, and event forecasting11
. We show that such learning methods may be used
to establish a mapping between a suitable representation of a material (i.e., its ‘fingerprint’ or its ‘profile’) and any
or all of its properties using known historic, or intentionally generated, data. The material fingerprint or profile
can be coarse-level chemo-structural descriptors, or something as fundamental as the electronic charge density,
both of which are explored here. Subsequently, once the profile u property mapping has been established, the
properties of a vast number of new materials within the same subclass may then be directly predicted (and
correlations between properties may be unearthed) at negligible computational cost, thereby completely by-
passing the conventional laborious approaches towards material property determination alluded to above. In its
most simplified form, this scheme is inspired by the intuition that (dis)similar materials will have (dis)similar
properties. Needless to say, training of this intuition requires a critical amount of prior diverse information/
results12–16
and robust learning devices12,17–22
.
The central problem in learning approaches is to come up with decision rules that will allow us to establish a
mapping between measurable (and easily accessible) attributes of a system and its properties. Quantum
mechanics (here employed within the framework of density functional theory, DFT)23,24
, provides us with such
a decision rule that connects the wave function (or charge density) with properties via the Schro¨dinger’s (or the
Kohn-Sham) equation. Here, we hope to replace the rather cumbersome rule based on the Schro¨dinger’s or Kohn-
Sham equation with a module based on similarity-based machine learning. The essential ingredients of the
proposed scheme is captured schematically in Figure 1.
SCIENTIFIC REPORTS SREP-13-02863-T.3d 18/9/13 12:57:56
OPEN
SUBJECT AREAS:
POLYMERS
ELECTRONIC STRUCTURE
COMPUTATIONAL METHODS
ELECTRONIC PROPERTIES AND
MATERIALS
Received
25 June 2013
Accepted
9 September 2013
Published
30 September 2013
Correspondence and
requests for materials
should be addressed to
R.R. (rampi@ims.
uconn.edu)
SCIENTIFIC REPORTS | 3 : 2810 | DOI: 10.1038/srep02810 1

Results
The ideal testing ground for such a paradigm is a case where a parent
material is made to undergo systematic chemical and/or configura-
tional variations, for which controlled initial training and test data
can be generated. In the present investigation, we consider infinite
polymer chains—quasi 1-d material motifs (Figure 1)—with their
building blocks drawn from a pool of the following seven possibil-
ities: CH2, SiF2, SiCl2, GeF2, GeCl2, SnF2, and SnCl2. Setting all the
building blocks of a chain to be CH2 leads to polyethylene (PE), a
common, inexpensive polymeric insulator. The rationale for intro-
ducing the other Group IV halides is to interrogate the beneficial
effects (if any) these blocks may have on various properties when
introduced in a base polymer such as PE. The properties that we will
focus on include: the atomization energy, the formation energy, the
lattice constant, the spring constant, the band gap, the electron affin-
ity, and the optical and static components of the dielectric constant.
The initial dataset for 175 such material motifs containing 4 building
blocks per repeat unit was generated using DFT.
The first step in the mapping process prescribed in the panels of
Figure 1 is to reduce each material system under inquiry to a string of
numbers—we refer to this string as the fingerprint vector. For the
specific case under consideration here, namely, polymeric chains
composed of seven possible building blocks, the following coarse-
level chemo-structural fingerprint vector was considered first: jf1, …,
f6, g1, …, g7, h1, …, h7æ, where fi, gi and hi are, respectively, the number
of building blocks of type i, number of i 2 i pairs, and number of i 2 i
2 i triplets, normalized to total number of units (note that f7 is
missing in above vector as it is not an independent quantity owing
to the relation: f7~1{
P6
i~1 fi). One may generalize the above vec-
tor to include all possible i 2 j pairs, i 2 j 2 k triplets, i 2 j 2 k 2 l
quadruplets, etc., but such extensions were found to be unnecessary
as the chosen 20-component vector was able to satisfactorily codify
the information content of the polymeric chains.
Next, a suitable measure of chemical distance is defined to allow for
a quantification of the degree of (dis)similarity between any two
fingerprint vectors. Consider two systems a and b with fingerprint
vectors~Fa
and~Fb
. The similarity of the two vectors may be measured
in many ways, e.g., using the Euclidean norm of the difference
between the two vectors, ~Fa
{~Fb

, or the dot product of the two
vectors~Fa:~Fb
. In the present work, we use the former, which we refer
to as ~Fab

(Figure 1). Clearly, if ~Fab

~0, materials a and b are
equivalent (insofar as we can conclude based on the fingerprint
vectors), and their property values Pa
and Pb
are the same. When
~Fab

=0, materials a and b are not equivalent, and Pa
2 Pb
is not
necessarily zero, and depends on ~Fab

. This observation may be
formally quantified when we have a prior materials-property dataset,
in which case we can determine the parametric dependence of the
property values on ~Fab

.
In the present work, we apply the machine learning algorithm
referred to as kernel ridge regression (KRR)25,26
. to our family of
polymeric chains. Technical details on the KRR methodology are
provided in the Methods section of the manuscript. As mentioned
Figure 1 | The machine (or statistical) learning methodology. First, material motifs within a class are reduced to numerical fingerprint vectors. Next, a
suitable measure of chemical (dis)similarity, or chemical distance, is used within a learning scheme—in this case, kernel ridge regression—to map the
distances to properties.
www.nature.com/scientificreports

above, the initial dataset was generated using DFT for systems with
repeat units containing 4 distinct building blocks. Of the total 175
such systems, 130 were classified to be in the ‘training’ set (used in the
training of the KRR model, Equation (1)), and the remainder in the
‘test’ set. Figure 2 shows the agreement between the predictions of the
learning model and the DFT results for the training and the test sets,
for each of the 8 properties examined. Furthermore, we considered
several chains composed of 8-block repeat units (in addition to the
175 4-block systems), performed DFT computations on these, and
compared the DFT predictions of the 8-block systems with those
predicted using our learning scheme. As can be seen, the level of
agreement between the DFT and the learning schemes is uniformly
good for all properties across the 4-block training and test set, as well
as the somewhat out-of-sample 8-block test set (regardless of the
variance in the property values). Moreover, properties controlled
by the local environment (e.g., the lattice parameter), as well as those
controlled by nonlocal global effects (e.g., the electronic part of the
dielectric constant) are well-captured. We do note that the agreement
is most spectacular for the energies than for the other properties (as
the former are most well-converged, and latter are derived or extra-
polated properties; see Methods). Overall, the high fidelity nature of
the learning predictions is particularly impressive, given that these
calculations take a minuscule fraction of the time necessitated by a
typical DFT computation.
While the favorable agreement between the machine learning and
the DFT results for a variety of properties is exciting, in and of itself,
the real power of this prediction paradigm lies in the possibility of
exploring a much larger chemical-configurational space than is prac-
tically possible using DFT computations (or laborious experimenta-
tion). For instance, merely expanding into a family of 1-d systems
with 8-block repeat units leads to 29,365 symmetry unique cases (an
extremely small fraction of this class was scrutinized above for valid-
ation purposes). Not only can the learning approach make the study
of this staggeringly large number of cases possible, it also allows for a
search for correlations between properties in a systematic manner. In
order to unearth such correlations, we first determined the properties
of the 29,365 systems using our machine learning methodology,
followed by the estimation of Pearson’s correlation coefficient for
each pair of properties. The Pearson correlation coefficient (r) used
to quantify a correlation between two given property datasets {Xi}
and {Yi} for a class of n material systems is defined as follows:
r~
Pn
i~1 Xi{Xð Þ Yi{Yð Þ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPn
i~1 Xi{Xð Þ
2
q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPn
i~1 Yi{Yð Þ
2
q : ð1Þ
Here, X and Y represent the average values of the properties over the
respective datasets. Figure 3a shows a matrix of the correlation coef-
ficients, color-coded to allow for immediate identification of pairs of
properties that are most correlated.
It can be seen from Figure 3a that the band gap is most strongly
correlated with many of the properties. Panels p1–p6 of Figure 3b
explicitly show the correlation between the band gap and six of the
remaining seven properties. Most notably, the band gap is inversely
correlated with the atomization energy (p1), size (p2), electron affin-
ity (p4), and the dielectric constants (p5 and p6), and directly corre-
lated with the spring constant (p3). The relationships captured in
panels p1–p3 follow from stability and bond strength arguments.
The interesting inverse relationship between the band gap and the
electron affinity is a consequence of the uniform shift of the conduc-
tion band minimum (due to changes in the band gap) with respect to
the vacuum level. The inverse correlation of the band gap with the
electronic part of the dielectric constant follows from the quantum
mechanical picture of electronic polarization being due to electronic
excitations. As no such requirement is expected for the ionic part of
the dielectric constant, it is rather surprising that a rough inverse
correlation is seen between the total dielectric constant and the band
gap, although clear deviations from this inverse behavior can be seen.
Finally, we note that the formation energy is uncorrelated with all the
other seven properties, including the band gap. This is particularly
notable as it is a common tendency to assume that the formation
energy (indicative of thermodynamic stability) is inversely correlated
with the band gap (indicative of electronic stability).
Discussion
Correlation diagrams such as the ones in Figure 3b offer a pathway to
‘design’ systems that meet a given set of property requirements. For
Figure 2 | Learning performance of chemo-structural fingerprint vectors. Parity plots comparing property values computed using DFT against
predictions made using learning algorithms trained using chemo-structural fingerprint vectors. Pearson’s correlation index is indicated in each of the
panels to quantify the agreement between the two schemes.

instance, a search for insulators with high dielectric constant and
large band gap would lead to those systems that are at the top part
of panel p6 of Figure 3b (corresponding to the ‘deviations’ from the
inverse correlation alluded to above, and indicated by a circle in panel
p6). These are systems that contain 2 or more contiguous SnF2 units,
but with an overall CH2 mole fraction of at least 25%. Such organo-
tin systems may be particularly appropriate for applications requir-
ing high-dielectric constant polymers. Furthermore, such diagrams
can aid in the extraction of knowledge from data eventually leading
to Hume-Rothery-like semi-empirical rules that dictate materials
behavior. For instance, the panel p3 reveals a well known corres-
pondence between mechanical strength and chemical stability27
,
while panels p5 and p6 capture an inverse relationship between the
dielectric constant and the bandgap, also quite familiar to the semi-
conductor physics community28
.
The entire discussion thus far has focused on fingerprint vectors
defined in terms of coarse-level chemo-structural descriptors. This
brings up a question as to whether other more fundamental quant-
ities may be used as a fingerprint to profile a material. The first
Hohenberg-Kohn theorem of DFT29
proves that the electronic
charge density of a system is a universal descriptor containing the
sum total of the information about the system, including all its prop-
erties. The shape30
and the holographic31
electron density theorems
constitute further extensions of the original Hohenberg-Kohn the-
orem. Inspired by these theorems, we propose that machine learning
methods may be used to establish a mapping between the electronic
charge density and various properties.
A fundamental issue related to this perspective deals with defining
a (dis)similarity criterion that can enable a fair comparison between
the charge density of two different systems. Note that any such mea-
sure has to be invariant with respect to relative translations and/or
rotations of the systems. In the present work, we have employed
Fourier coefficients of the 1-d charge density of our systems (aver-
aged along the plane normal to the chain axis). The Fourier coeffi-
cients are invariant to translations of the systems along the chain axis,
and consideration of the 1-d planar averaged charge density makes
the rotational degrees of freedom irrelevant. Figure 4 shows a com-
parison of the predictions of the learning model based on charge
density with the corresponding DFT results. While the agreement
between the learning scheme and DFT is not as remarkable as with
the chemo-structural fingerprint approach adopted earlier, this can
most likely be addressed by the utilization of the actual 3-d charge
density. Nevertheless, we believe that the performance of the learning
scheme is satisfactory, and heralds the possibility of arriving at a
‘universal’ approach for property predictions solely using the elec-
tronic charge density.
A second issue with the charge density based materials profiling
relates to determining the charge density in the first place. If indeed a
mapping between charge density and the properties can be made for
the training set, how do we obtain the charge density of a new system
without explicitly performing a DFT computation? We suggest that
the ‘atoms in molecules’ concept may be exploited to create a
patched-up charge density distribution32
. Needless to say, barring
some studies in the area of atoms and molecules33
, these concepts
are in a state of infancy, and there is much room available for both
fundamental developments and innovative applications.
To conclude, we have shown that the efficient and accurate pre-
diction of a diverse set of unrelated properties of material systems is
possible by combining the notions of chemical (dis)similarity and
machine (or statistical) learning methods. Using a family of 1-d chain
systems, we have presented a general formalism that allows us to
discover decision rules that establish a mapping between easily
accessible attributes of a system and its various properties. We have
unambiguously shown that simple fingerprint vectors based on
either compositional and configurational information, or the elec-
tronic charge density distribution, can be used to profile a material
and make property predictions at an enormously small cost com-
pared either with quantum mechanical calculations or laborious
experimentation. The methodology presented here is of direct rel-
evance in identifying (or screening) undiscovered materials in a tar-
geted class with desired combination of properties in an efficient
manner with high fidelity.
Methods
First principles computations. The quantum mechanical computations were
performed using density functional theory (DFT)23,24
as implemented in the Vienna
ab initio software package34,35
. The generalized gradient approximation (GGA)
functional parametrized by Perdew, Burke and Ernzerhof (PBE)36
to treat the
electronic exchange-correlation interaction, the projector augmented wave (PAW)37
Figure 3 | High throughput predictions and correlations from machine learning. (a) The upper triangle presents a schematic of the atomistic
model composed of repeat units with 8 building blocks. Populating each of he 8 blocks with one of the seven units leads to 29,365 systems. The matrix in
the lower triangle depicts the Pearson’s correlation index for each pair of the eight properties of the 8-block systems predicted using machine learning.
(b) Panels p1 to p6 show the correlations between the band gap and six properties. The panel labels are also appropriately indexed in (a). The circle in
panel p6 indicates systems with a simultaneously large dielectric constant and band gap.

potentials, and plane-wave basis functions up to a kinetic energy cutoff of 500 eV
were employed.
Our 1-d systems were composed of all-trans infinitely long isolated chains con-
taining 4 independent building units in a supercell geometry (with periodic boundary
conditions along the axial direction). One CH2 unit was always retained in the
backbone (to break the extent of s-conjugation along the backbone), and the three
other units were drawn from a ‘‘pool’’ of seven possibilities: CH2, SiF2, SiCl2, GeF2,
GeCl2, SnF2 and SnCl2, in a combinatorial and exhaustive manner. This scheme
resulted in 175 symmetry unique systems after accounting for translational peri-
odicity and inversion symmetry. A Monkhorst-Pack k-point mesh of 1 3 1 3 k (with
kc . 50) was used to produce converged results for a supercell of length c A˚ along the
chain direction (i.e., the z direction). The supercells were relaxed using a conjugate
gradient algorithm until the forces on all atoms were ,0.02 eV/A˚ and the stress
component along the z direction was ,1.0 3 1022
GPa. Sufficiently large grids were
used to avoid numerical errors in fast Fourier transforms. A small number of cases
involving 8 building units were also performed for validation purposes.
The calculated atomization energies and formation energies are referenced to the
isolated atoms and homo-polymer chains of the constituents, respectively. While the
lattice parameters, spring constants, band gaps and electron affinities of the systems
are readily accessible through DFT computations, the calculations of the optical and
static components of the dielectric constant require particular care. The dielectric
permittivity of the isolated polymer chains placed in a large supercell were first
computed within the density functional perturbation theory (DFPT)38,39
formalism,
which includes contributions from the polymer as well as from the surrounding
vacuum region of the supercell. Next, treating the supercell as a vacuum-polymer
composite, effective medium theory40
was used to estimate the dielectric constant of
just the polymer chains using methods described recently13,41
. Table 1 of the
Supporting Information contains the DFT computed atomization energies, forma-
tion energies, c lattice parameters, spring constants, electron affinities, bandgaps, and
dielectric permittivities for the 175 symmetry unique polymeric systems.
Machine learning details. Within the present similarity-based learning model, a
property of a system in the test set is given by a sum of weighted Gaussians over the
entire training set, as
Pb
~
XN
a~1
aa exp {
1
2s2
Fab

2

: ð2Þ
where a runs over the systems in the previously known dataset. The coefficients aas
and the parameter s are obtained by ‘training’ the above form on the systems a in the
previously known dataset. The training (or learning) process is built on minimizing
the expression
PN
a~1
Pa
Est{Pa
DFT
À Á2
zl
PN
a~1
a2
a, with Pa
Est being the estimated property
value, Pa
DFT the DFT value, and l a regularization parameter25,26
. The explicit solution
to this minimization problem is a 5 (K 1 lI)21
PDFT, where I is the identity matrix,
and Kab~exp {
1
2s2
Fab

2

is the kernel matrix elements of all polymers in the
training set. The parameters l, s and aas are determined in an inner loop of fivefold
cross validation using a logarithmically scaling fine grid.
1. Poggio, T., Rifkin, R., Mukherjee, S. Niyogi, P. General conditions for
predictivity in learning theory. Nature 428, 419–422 (2004).
2. Tomasi, C. Past performance and future results. Nature 428, 378 (2004).
3. Rehmeyer, J. Influential few predict behavior of the many. Nature News, http://dx.
doi.org/10.1038/nature.2013.12447.
4. Holland, J. H. Emergence: from Chaos to order (Cambridge, Perseus, 1998).
5. Jones, N. Quiz-playing computer system could revolutionize research. Nature
News, http://dx.doi.org/10.1038/news.2011.95.
6. MacLeod, N., Benfield, M. Culverhouse, P. Time to automate identification.
Nature 467, 154–155 (2010).
7. Crutchfield, J. P. Between order and chaos. Nature Physics 8, 17–24 (2012).
8. Chittka, L. Dyer, A. Cognition: Your face looks familiar. Nature 481, 154–155
(2012).
9. Abu-Mostafa, Y. S. Machines that Learn from Hints. Sci. Am. 272, 64–69 (1995).
10. Abu-Mostafa, Y. S. Machines that Think for Themselves. Sci. Am. 307, 78–81
(2012).
11. Silver, N. The Signal and the Noise: Why So Many Predictions Fail but Some Don’t
(Penguin Press, New York, 2012).
12. Curtarolo, S. et al. The high-throughput highway to computational materials
design. Nature Mater. 12, 191–201 (2013).
13. Pilania, G. et al. New group IV chemical motifs for improved dielectric
permittivity of polyethylene. J. Chem. Inf. Modeling 53, 879–886 (2013).
14. Levy, O., Hart, G. L. W. Curtarolo, S. Uncovering compounds by synergy of
cluster expansion and high-throughput methods. J. Am. Chem. Soc. 132,
4830–4833 (2010).
15. Jain, A. et al. A high-throughput infrastructure for density functional theory
calculations. Comp. Mater. Sci. 50, 22952310 (2011).
16. Hart, G. L. W., Blum, V., Walorski, M. J. Zunger, A. Evolutionary approach for
determining first-principles hamiltonians. Nature Mater. 4, 391394 (2005).
17. Fischer, C. C., Tibbetts, K. J., Morgan, D. Ceder, G. Predicting crystal structure
by merging data mining with quantum mechanics. Nature Mater. 5, 641–646
(2006).
18. Saad, Y. et al. Data mining for materials: Computational experiments with AB
compounds. Phys. Rev. B 85, 104104 (2012).
19. Rupp, M., Tkatchenko, A., Muller, K. von Lilienfeld, O. A. Fast and accurate
modeling of molecular atomization energies with machine learning. Phys. Rev.
Lett. 108, 058301 (2012).
20. Snyder, J. C., Rupp, M., Hansen, K., Mu¨ller, K.-R. Burke, K. Finding Density
Functionals with Machine Learning. Phys. Rev. Lett. 108, 253002 (2012).
Figure 4 | Learning performance of electron charge density-based fingerprint vectors. Parity plots comparing property values computed using DFT
against predictions made using learning algorithms trained using electron density-based fingerprint vectors. The Fourier coefficients of the planar-
averaged Kohn-Sham charge density are used to construct the fingerprint vector. Pearson’s correlation index is indicated in each of the panels to quantify
the agreement between the two schemes.

21. Montavon, G. et al. Machine Learning of Molecular Electronic Properties in
Chemical Compound Space. Accepted to New J. Phys.
22. Hautier, G., Fisher, C. C., Jain, A., Mueller, T. Ceder, G. Finding natures missing
ternary oxide compounds using machine learning and density functional theory.
Chem. Mater. 22, 3762 (2010).
23. Kohn, W. Electronic structure of matter–wave functions and density functionals.
Rev. Mod. Phys. 71, 1253 (1999).
24. Martin, R. Electronic Structure: Basic Theory and Practical Methods (Cambridge
University Press, New York, 2004).
25. Hastie, T., Tibshirani, R. Friedman, J. The Elements of Statistical Learning: Data
Mining, Inference, and Prediction (Springer, New York, 2009).
26. Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K. Scholkopf, B. An introduction to
kernel-based learning algorithms. IEEE Transactions on Neural Networks 12, 181
(2001).
27. Gilman, J. J. Physical chemistry of intrinsic hardness. Mater. Sci. and Eng. A209,
74–81 (1996).
28. Zhu, H., Tang, C., Fonseca, L. R. C. Ramprasad, R. Recent progress in ab initio
simulations of hafnia-based gate stacks. J. Mater. Sci. 47, 7399–7416 (2012).
29. Hohenberg, P. Kohn, W. Inhomogeneous electron gas. Phys. Rev. 136,
B864–B871 (1964).
30. Geerlings, P., Boon, G., Van Alsenoy, C. De Proft, F. Density functional theory
and quantum similarity. Int. J. Quantum. Chem. 101, 722 (2005).
31. Mezey, P. G. Holographic electron density shape theorem and its role in drug
design and toxicological risk assessment. J. Chem. Inf. Comput. Sci. 39, 224 (1999).
32. Bader, R. F. W. Atoms in molecules: a quantum theory (Oxford University Press,
Oxford, 1990).
33. Bultinck, P., Girones, X. Carbo-Dorca, R. Molecular quantum similarity: theory
and applications. Reviews in Computational Chemistry, Volume 21 (2005).
34. Kresse, G. Furthmuller, J. Efficient iterative schemes for ab initio total-energy
calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).
35. Kresse, G. Furthmuller, J. Efficiency of ab-initio total energy calculations for
metals and semiconductors using a plane-wave basis set. J. Comput. Mater. Sci. 6,
15–50 (1996).
36. Perdew, J., Burke, K. Ernzerhof, M. Generalized Gradient Approximation Made
Simple. Phys. Rev. Lett. 77, 3865–3868 (1996).
37. Blo¨chl, P. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979
(1994).
38. Baroni, S., de Gironcoli, S. Dal Corso, A. Phonons and related crystal properties
from density-functional perturbation theory. Rev. Mod. Phys. 73, 515–562 (2001).
39. Gonze, X. Dynamical matrices, Born effective charges, dielectric permittivity
tensors, and interatomic force constants from density-functional perturbation
theory. Phys. Rev. B 55, 10355–10368 (1997).
40. Choy, T. C. Effective medium theory: principles and applications (Oxford
University Press Inc., Oxford, 1999).
41. Wang, C. C., Pilania, G. Ramprasad, R. Dielectric properties of carbon-, silicon-,
and germanium-based polymers: A first-principles study. Phys. Rev. B 87, 035103
(2013).
Acknowledgements
This paper is based upon work supported by a Multidisciplinary University Research
Initiative (MURI) grant from the Office of Naval Research. Computational support was
provided by the Extreme Science and Engineering Discovery Environment (XSEDE), which
is supported by National Science Foundation. Discussions with Kenny Lipkowitz, Ganpati
Ramanath and Gerbrand Ceder are gratefully acknowledged.
Author contributions
R.R., C.W. and G.P. conceived the statistical learning model, with input from S.R. and X.J.
The DFT computations were performed by G.P. The initial implementation of the statistical
learning framework was performed by C.W. and extended by G.P. The manuscript was
written by G.P., S.R. and R.R.
Additional information
Supplementary information accompanies this paper at http://www.nature.com/
scientificreports
Competing financial interests: The authors declare no competing financial interests.
How to cite this article: Pilania, G., Wang, C., Jiang, X., Rajasekaran, S. Ramprasad, R.
Accelerating materials property predictions using machine learning. Sci. Rep. 3, 2810;
DOI:10.1038/srep02810 (2013).
This work is licensed under a Creative Commons Attribution 3.0 Unported license.
To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0

Accelerating materials property predictions using machine learning

More Related Content

What's hot (20)

Viewers also liked (8)

Similar to Accelerating materials property predictions using machine learning (20)

Recently uploaded (20)

Accelerating materials property predictions using machine learning