Machine learning (ML) methods have the potential to revolutionize materials design, due to their ability to screen materials efficiently. Unlike other popular applications such as image recognition or language processing, large volumes of data are not available for materials design applications. Here, we first show that a standard learning approach using generic descriptors does not work for small data, unless it is guided by insights from physical equations. We then propose a novel method for transferring such physical insights onto more generic descriptors, allowing us to screen billions of unknown compositions for Li-ion conductivity, a scale which was previously unfeasible. This is accomplished by using the accurate model trained with physical insights to create a large database, on which we train a new ML model using the generic descriptors. Unlike previous applications of ML, this approach allows us to screen materials which have not necessarily been tested before (i.e., not on ICSD or Materials Project). Our method can be applied to any materials design application where a small amount of data is available, combined with high details of physical understanding.
REFERENCES
1.
Y.
Saad
, D.
Gao
, T.
Ngo
, S.
Bobbitt
, J. R.
Chelikowsky
, and W.
Andreoni
, “Data mining for materials: Computational experiments with a b compounds
,” Phys. Rev. B
85
, 104104
(2012
).2.
A.
Seko
, A.
Togo
, H.
Hayashi
, K.
Tsuda
, L.
Chaput
, and I.
Tanaka
, “Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and bayesian optimization
,” Phys. Rev. Lett.
115
, 205901
(2015
).3.
L. M.
Ghiringhelli
, J.
Vybiral
, S. V.
Levchenko
, C.
Draxl
, and M.
Scheffler
, “Big data of materials science: Critical role of the descriptor
,” Phys. Rev. Lett.
114
, 105503
(2015
).4.
J.
Lee
, A.
Seko
, K.
Shitara
, K.
Nakayama
, and I.
Tanaka
, “Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques
,” Phys. Rev. B
93
, 115104
(2016
).5.
A.
Seko
, T.
Maekawa
, K.
Tsuda
, and I.
Tanaka
, “Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single-and binary-component solids
,” Phys. Rev. B
89
, 054303
(2014
).6.
L.
Xu
, L.
Wencong
, P.
Chunrong
, S.
Qiang
, and G.
Jin
, “Two semi-empirical approaches for the prediction of oxide ionic conductivities in abo 3 perovskites
,” Comput. Mater. Sci.
46
, 860
–868
(2009
).7.
K.
Fujimura
, A.
Seko
, Y.
Koyama
, A.
Kuwabara
, I.
Kishida
, K.
Shitara
, C. A.
Fisher
, H.
Moriwake
, and I.
Tanaka
, “Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
,” Adv. Energy Mater.
3
, 980
–985
(2013
).8.
A. D.
Sendek
, Q.
Yang
, E. D.
Cubuk
, K.-A. N.
Duerloo
, Y.
Cui
, and E. J.
Reed
, “Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
,” Energy Environ. Sci.
10
, 306
–320
(2017
).9.
A.
Jain
, S. P.
Ong
, G.
Hautier
, W.
Chen
, W. D.
Richards
, S.
Dacek
, S.
Cholia
, D.
Gunter
, D.
Skinner
, G.
Ceder
, and K. A.
Persson
, “The materials Project: A materials genome approach to accelerating materials innovation
,” APL Mater.
1
, 011002
(2013
).10.
L.
Ward
, A.
Agrawal
, A.
Choudhary
, and C.
Wolverton
, “A general-purpose machine learning framework for predicting properties of inorganic materials
,” npj Comput. Mater.
2
, 16028
(2016
).11.
C.
Cortes
and V.
Vapnik
, “Support-vector networks
,” Mach. Learn.
20
, 273
–297
(1995
).12.
C.-C.
Chang
and C.-J.
Lin
, “LIBSVM: A library for support vector machines
,” ACM Trans. Intell. Syst. Technol.
2
, 1
–27
(2011
).13.
L. G.
Valiant
, “A theory of the learnable
,” Commun. ACM
27
, 1134
–1142
(1984
).14.
A. D.
Sendek
, E. D.
Cubuk
, E. R.
Antoniuk
, G.
Cheon
, Y.
Cui
, and E. J.
Reed
, “Machine learning-assisted discovery of solid Li-ion conducting materials
,” Chem. Mater.
31
, 342
–352
(2019
).15.
Y.
LeCun
, Y.
Bengio
, and G.
Hinton
, “Deep learning
,” Nature
521
, 436
–444
(2015
).16.
J.
Behler
and M.
Parrinello
, “Generalized neural-network representation of high-dimensional potential-energy surfaces
,” Phys. Rev. Lett.
98
, 146401
(2007
).17.
Y.
He
, E. D.
Cubuk
, M. D.
Allendorf
, and E. J.
Reed
, “Metallic metal–organic frameworks predicted by the combination of machine learning methods and ab initio calculations
,” J. Phys. Chem. Lett.
9
, 4562
–4569
(2018
).18.
P. Z.
Hanakata
, E. D.
Cubuk
, D. K.
Campbell
, and H. S.
Park
, “Accelerated search and design of stretchable Graphene Kirigami using machine learning
,” Phys. Rev. Lett.
121
, 255304
(2018
).19.
E. D.
Cubuk
, B. D.
Malone
, B.
Onat
, A.
Waterland
, and E.
Kaxiras
, “Representations in neural network based empirical potentials
,” J. Chem. Phys.
147
, 024104
(2017
).20.
B.
Onat
, E. D.
Cubuk
, B. D.
Malone
, and E.
Kaxiras
, “Implanted neural network potentials: Application to Li-Si alloys
,” Phys. Rev. B
97
, 094106
(2018
).21.
L.
Bassman
, P.
Rajak
, R. K.
Kalia
, A.
Nakano
, F.
Sha
, J.
Sun
, D. J.
Singh
, M.
Aykol
, P.
Huck
, K.
Persson
, and P.
Vashishta
, “Active learning for accelerated design of layered materials
,” npj Comput. Mater.
4
, 74
(2018
).22.
J.
Hoffmann
, Y.
Bar-Sinai
, L.
Lee
, J.
Andrejevic
, S.
Mishra
, S. M.
Rubinstein
, and C. H.
Rycroft
, “Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
,” Sci. Adv.
5
(4
), eaau6792
(2019
).23.
G.
Cheon
, E. D.
Cubuk
, E. R.
Antoniuk
, L.
Blumberg
, J. E.
Goldberger
, and E. J.
Reed
, “Revealing the spectrum of unknown layered materials with superhuman predictive abilities
,” J. Phys. Chem. Lett.
9
, 6967
–6972
(2018
).24.
L.
Ward
, R.
Liu
, A.
Krishna
, V. I.
Hegde
, A.
Agrawal
, A.
Choudhary
, and C.
Wolverton
, “Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
,” Phys. Rev. B
96
, 024104
(2017
).25.
F.
Ren
, L.
Ward
, T.
Williams
, K. J.
Laws
, C.
Wolverton
, J.
Hattrick-Simpers
, and A.
Mehta
, “Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
,” Sci. Adv.
4
, eaaq1566
(2018
).26.
N.
Artrith
, B.
Hiller
, and J.
Behler
, “Neural network potentials for metals and oxides–First applications to copper clusters at zinc oxide
,” Phys. Status Solidi B
250
, 1191
–1203
(2013
).27.
A.
Belsky
, M.
Hellenbrandt
, V. L.
Karen
, and P.
Luksch
, “New developments in the inorganic crystal structure database (ICSD): Accessibility in support of materials research and design
,” Acta Crystallogr., Sect. B: Struct. Sci.
58
, 364
–369
(2002
).28.
S. J.
Pan
and Q.
Yang
, “A survey on transfer learning
,” IEEE Trans. Knowl. Data Eng.
22
, 1345
–1359
(2010
).29.
B. D.
McCloskey
, “Attainable gravimetric and volumetric energy density of Li–S and Li ion battery cells with solid separator-protected Li metal anodes
,” J. Phys. Chem. Lett.
6
, 4581
–4588
(2015
).30.
J.
Xie
, A. D.
Sendek
, E. D.
Cubuk
, X.
Zhang
, Z.
Lu
, Y.
Gong
, T.
Wu
, F.
Shi
, W.
Liu
, E. J.
Reed
, and Y.
Cui
, “Atomic layer deposition of stable LiAlF4 lithium ion conductive interfacial layer for stable cathode cycling
,” ACS Nano
11
, 7019
–7027
(2017
).31.
M.
Vogel
, “Complex lithium ion dynamics in simulated LiPo3 glass studied by means of multitime correlation functions
,” Phys. Rev. B
68
, 184301
(2003
).32.
Q.
Zhu
, A. R.
Oganov
, and A. O.
Lyakhov
, “Novel stable compounds in the mg–o system under high pressure
,” Phys. Chem. Chem. Phys.
15
, 7696
–7700
(2013
).33.
T.
Mikolov
, I.
Sutskever
, K.
Chen
, G. S.
Corrado
, and J.
Dean
, “Distributed representations of words and phrases and their compositionality
,” in NIPS’13 Proceedings of the 26th International Conference on Neural Information Processing Systems
, Vol. 2, pp. 3111
–3119
(2013
).34.
Q.
Zhou
, P.
Tang
, S.
Liu
, J.
Pan
, Q.
Yan
, and S.-C.
Zhang
, “Learning atoms for materials discovery
,” Proc. Natl. Acad. Sci. U. S. A.
115
, 201801181
(2018
).© 2019 Author(s).
2019
Author(s)
You do not currently have access to this content.
