We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.

1.
M.
Hilbert
and
P.
López
,
Science
332
,
60
(
2011
).
2.
I.
Goodfellow
,
Y.
Bengio
, and
A.
Courville
,
Deep Learning
(
MIT Press
,
2016
).
3.
G.
Rubio
,
N.
Agrait
, and
S.
Vieira
,
Phys. Rev. Lett.
76
,
2302
(
1996
).
4.
S. V.
Aradhya
,
A.
Nielsen
,
M. S.
Hybertsen
, and
L.
Venkataraman
,
ACS Nano
8
,
7522
(
2014
).
5.
I. V.
Pobelov
,
K. P.
Lauritzen
,
K.
Yoshida
,
A.
Jensen
,
G.
Mészáros
,
K. W.
Jacobsen
,
M.
Strange
,
T.
Wandlowski
, and
G. C.
Solomon
,
Nat. Commun.
8
,
15931
(
2017
).
6.
Z.
Huang
,
F.
Chen
,
P. A.
Bennett
, and
N.
Tao
,
J. Am. Chem. Soc.
129
,
13225
(
2007
).
7.
G.
Rubio-Bollinger
,
S. S.
Bahn
,
N.
Agraït
,
K.
Jacobsen
, and
S.
Vieira
,
Phys. Rev. Lett.
87
,
026101
(
2001
).
8.
A. I.
Yanson
,
G. R.
Bollinger
,
H. E.
van den Brom
,
N.
Agraït
, and
J. M.
van Ruitenbeek
,
Nature
395
,
783
(
1998
).
9.
N.
Agrait
,
A.
Yeyatib
, and
J. M.
van Ruitenbeek
,
Phys. Rep.
377
,
81
(
2003
).
10.
E. Z.
da Silva
,
A. J. R.
da Silva
, and
A.
Fazzio
,
Phys. Rev. Lett.
87
,
256102
(
2001
).
11.
S. R.
Bahn
and
K. W.
Jacobsen
,
Phys. Rev. Lett.
87
,
266101
(
2001
).
12.
M. R.
Sørensen
,
M.
Brandbyge
, and
K. W.
Jacobsen
,
Phys. Rev. B
57
,
3283
(
1998
).
13.
A.
Nakamura
,
M.
Brandbyge
,
L. V.
Hansen
, and
K. W.
Jacobsen
,
Phys. Rev. Lett.
82
,
1538
(
1999
).
14.
P.
Makk
,
D.
Visontai
,
L.
Oroszlány
,
D. Z.
Manrique
,
S.
Csonka
,
J.
Cserti
,
C.
Lambert
, and
A.
Halbritter
,
Phys. Rev. Lett.
107
,
276801
(
2011
).
15.
R.
Vardimon
,
M.
Matt
,
P.
Nielaba
,
J. C.
Cuevas
, and
O.
Tal
,
Phys. Rev. B
93
,
085439
(
2015
); e-print arXiv:1512.02601.
16.
M.
Paulsson
,
C.
Krag
,
T.
Frederiksen
, and
M.
Brandbyge
,
Nano Lett.
9
,
117
(
2009
).
17.
Q.
Pu
,
Y.
Leng
,
X.
Zhao
, and
P. T.
Cummings
,
J. Phys. Chem. C
114
,
10365
(
2010
).
18.
H.
Wang
and
Y.
Leng
,
J. Phys. Chem. C
119
,
15216
(
2015
).
19.
P.
Moreno-García
,
M.
Gulcur
,
D. Z.
Manrique
,
T.
Pope
,
W.
Hong
,
V.
Kaliginedi
,
C.
Huang
,
A. S.
Batsanov
,
M. R.
Bryce
,
C.
Lambert
, and
T.
Wandlowski
,
J. Am. Chem. Soc.
135
,
12228
(
2013
).
20.
N.
Agrait
,
G.
Rubio
, and
S.
Vieira
,
Phys. Rev. Lett.
74
,
3995
(
1995
).
21.
D. A.
Wharam
,
T. J.
Thornton
,
R.
Newbury
,
M.
Pepper
,
H.
Ahmed
,
J. E. F.
Frost
,
D. G.
Hasko
,
D. C.
Peacock
,
D. A.
Ritchie
, and
G. A. C.
Jones
,
J. Phys. C: Solid State Phys.
21
,
L209
(
1988
).
22.
H.
Ohnishi
,
Y.
Kondo
, and
K.
Takayanagi
,
Nature
395
,
780
(
1998
).
23.
24.
NIST Big Data Public Working Group
,
NIST Spec. Publ.
1
,
32
(
2015
).
25.
J. M.
Hamill
,
X. T.
Zhao
,
G.
Mészáros
,
M. R.
Bryce
, and
M.
Arenz
,
Phys. Rev. Lett.
120
(
1
),
016601
(
2017
); e-print arXiv:1705.06161.
26.
M. S.
Inkpen
,
M.
Lemmer
,
N.
Fitzpatrick
,
D. C.
Milan
,
R. J.
Nichols
,
N. J.
Long
, and
T.
Albrecht
,
J. Am. Chem. Soc.
137
,
9971
(
2015
).
27.
M.
Lemmer
,
M. S.
Inkpen
,
K.
Kornysheva
,
N. J.
Long
, and
T.
Albrecht
,
Nat. Commun.
7
,
12922
(
2016
).
28.
P.
Makk
,
D.
Tomaszewski
,
J.
Martinek
,
Z.
Balogh
,
S.
Csonka
,
M.
Wawrzyniak
,
M.
Frei
,
L.
Venkataraman
, and
A.
Halbritter
,
ACS Nano
6
,
3411
(
2012
).
29.
K.
Jacobsen
,
J. K.
Norskov
, and
M. J.
Puska
,
Phys. Rev. B
35
,
7423
(
1987
).
30.
K.
Jacobsen
,
P.
Stoltze
, and
J.
Nørskov
,
Surf. Sci.
366
,
394
(
1996
).
31.
A.
Hjorth Larsen
,
J.
Jørgen Mortensen
,
J.
Blomqvist
,
I. E.
Castelli
,
R.
Christensen
,
M.
Dułak
,
J.
Friis
,
M. N.
Groves
,
B.
Hammer
,
C.
Hargus
,
E. D.
Hermes
,
P. C.
Jennings
,
P.
Bjerre Jensen
,
J.
Kermode
,
J. R.
Kitchin
,
E.
Leonhard Kolsbjerg
,
J.
Kubal
,
K.
Kaasbjerg
,
S.
Lysgaard
,
J.
Bergmann Maronsson
,
T.
Maxson
,
T.
Olsen
,
L.
Pastewka
,
A.
Peterson
,
C.
Rostgaard
,
J.
Schiøtz
,
O.
Schütt
,
M.
Strange
,
K. S.
Thygesen
,
T.
Vegge
,
L.
Vilhelmsen
,
M.
Walter
,
Z.
Zeng
, and
K. W.
Jacobsen
,
J. Phys.: Condens. Matter
29
,
273002
(
2017
).
32.
J.
Schiøtz
, ASAP—As Soon As Possible, https://wiki.fysik.dtu.dk/asap/,
2017
.
33.
M.
Dreher
,
F.
Pauly
,
J.
Heurich
,
J. C.
Cuevas
,
E.
Scheer
, and
P.
Nielaba
,
Phys. Rev. B
72
,
075435
(
2005
).
34.
E.
Scheer
,
N.
Agraït
,
J. C.
Cuevas
,
A. L.
Yeyati
,
B.
Ludoph
,
A.
Martín-Rodero
,
G. R.
Bollinger
,
J. M.
van Ruitenbeek
, and
C.
Urbina
,
Nature
394
,
154
(
1998
).
35.
M.
Nielsen
,
Neural Networks and Deep Learning
(
Determination Press
,
2015
).
36.
M.
Abadi
,
A.
Agarwal
,
P.
Barham
,
E.
Brevdo
,
Z.
Chen
,
C.
Citro
,
G. S.
Corrado
,
A.
Davis
,
J.
Dean
,
M.
Devin
,
S.
Ghemawat
,
I.
Goodfellow
,
A.
Harp
,
G.
Irving
,
M.
Isard
,
Y.
Jia
,
R.
Jozefowicz
,
L.
Kaiser
,
M.
Kudlur
,
J.
Levenberg
,
D.
Mané
,
R.
Monga
,
S.
Moore
,
D.
Murray
,
C.
Olah
,
M.
Schuster
,
J.
Shlens
,
B.
Steiner
,
I.
Sutskever
,
K.
Talwar
,
P.
Tucker
,
V.
Vanhoucke
,
V.
Vasudevan
,
F.
Viégas
,
O.
Vinyals
,
P.
Warden
,
M.
Wattenberg
,
M.
Wicke
,
Y.
Yu
, and
X.
Zheng
, eprint arXiv:1603.04467 [cs.DC].
37.
F.
Chollet
, Keras, https://github.com/fchollet/keras,
2017
.
38.
S.
Hochreiter
and
J.
Schmidhuber
,
Neural Comput.
9
,
1735
(
1997
).
39.
K.
Cho
,
B.
van Merrienboer
,
C.
Gulcehre
,
D.
Bahdanau
,
F.
Bougares
,
H.
Schwenk
, and
Y.
Bengio
, in (
Association for Computational Linguistics
,
2014
), pp.
1724
1734
; e-print arXiv:1406.1078.
40.
C. M.
Bishop
,
Pattern Recognition and Machine Learning
(
Springer
,
2006
).
41.
S.
Semeniuta
,
A.
Severyn
, and
E.
Barth
, e-print arXiv:1603.05118 (
2016
).
42.
A.
Magyarkuti
,
K.
Lauritzen
,
Z.
Balogh
,
A.
Nyáry
,
G.
Mészáros
,
P.
Makk
,
G. C.
Solomon
, and
A.
Halbritter
,
J. Chem. Phys.
146
,
092319
(
2017
); e-print arXiv:1612.01439.
43.
D. R.
Cox
,
J. R. Stat. Soc.
20
,
215
(
1958
).
44.
P. J.
Rousseeuw
,
J. Comput. Appl. Math.
20
,
53
(
1987
).
45.
F.
Pedregosa
,
G.
Varoquaux
,
A.
Gramfort
,
V.
Michel
,
B.
Thirion
,
O.
Grisel
,
M.
Blondel
,
P.
Prettenhofer
,
R.
Weiss
,
V.
Dubourg
 et al,
J. Mach. Learn. Res.
12
,
2825
(
2011
).
46.
A.
Mordvintsev
,
C.
Olah
, and
M.
Tyka
, Inceptionism: Going deeper into neural networks, https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html,
2015
.
47.
C.
Olah
,
A.
Mordvintsev
, and
L.
Schubert
, “
Feature Visualization
,”
Distill
(November 7,
2017
).
48.
I.
Goodfellow
,
J.
Pouget-Abadie
,
M.
Mirza
,
B.
Xu
,
D.
Warde-Farley
,
S.
Ozair
,
A.
Courville
, and
Y.
Bengio
, in
Advances in Neural Information Processing Systems 27
, edited by
Z.
Ghahramani
,
M.
Welling
,
C.
Cortes
,
N. D.
Lawrence
, and
K. Q.
Weinberger
(
Curran Associates, Inc.
,
2014
), pp.
2672
2680
.

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