Unsupervised vector-based classification of single-molecule charge transport data.
The stochastic nature of single-molecule charge transport measurements requires collection of large data sets to capture the full complexity of a molecular system. Data analysis is then guided by certain expectations, for example, a plateau feature in the tunnelling current distance trace, and the molecular conductance extracted from suitable histogram analysis. However, differences in molecular conformation or electrode contact geometry, the number of molecules in the junction or dynamic effects may lead to very different molecular signatures. Since their manifestation is a priori unknown, an unsupervised classification algorithm, making no prior assumptions regarding the data is clearly desirable. Here we present such an approach based on multivariate pattern analysis and apply it to simulated and experimental single-molecule charge transport data. We demonstrate how different event shapes are clearly separated using this algorithm and how statistics about different event classes can be extracted, when conventional methods of analysis fail.
Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.
Mario
M
Lemmer
Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.
Michael S
0000-0001-7339-8812
MS
Inkpen
Institute for Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, London WC1N 3AR, UK.
Katja
K
Kornysheva
Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.
Nicholas J
0000-0002-8298-938X
NJ
Long
Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.
Tim
0000-0001-6085-3206
T
Albrecht
Sci Rep. 2015 Mar 11;5:9002
25758349
J Phys Chem B. 2006 Feb 16;110(6):2462-6
16471840
Nanotechnology. 2012 Jun 15;23(23):235101
22609769
Nat Chem. 2015 Mar;7(3):215-20
25698330
ACS Nano. 2014 Feb 25;8(2):1940-8
24446951
ACS Nano. 2008 Nov 25;2(11):2315-23
19206398
ACS Nano. 2015 Jul 28;9(7):7704-13
26168212
J Am Chem Soc. 2012 Mar 21;134(11):5262-75
22352944
Nano Lett. 2012 May 9;12(5):2243-8
22494042
Nano Lett. 2011 Jan 12;11(1):279-85
21133389
Nano Lett. 2005 Jul;5(7):1451-5
16178256
Nature. 2006 Aug 24;442(7105):904-7
16929295
Neuroimage. 2011 May 15;56(2):400-10
20691790
J Am Chem Soc. 2006 Dec 27;128(51):17132-8
17177467
Nano Lett. 2015 Jun 10;15(6):4143-9
25942441
Science. 2014 Jun 27;344(6191):1492-6
24970081
J Am Chem Soc. 2010 Jun 16;132(23):7946-56
20433175
Nat Nanotechnol. 2008 Sep;3(9):569-74
18772920
J Am Chem Soc. 2007 Jul 25;129(29):9162-7
17583341
Nat Commun. 2012 May 08;3:829
22569373
Phys Chem Chem Phys. 2010 Mar 28;12(12):2801-15
20449369
Nano Lett. 2009 Apr;9(4):1442-6
19284729
J Am Chem Soc. 2012 Mar 7;134(9):4003-6
22338625
J Am Chem Soc. 2005 Jun 29;127(25):9235-40
15969602
J Am Chem Soc. 2008 Jan 23;130(3):1080-4
18163626
Phys Chem Chem Phys. 2006 Sep 7;8(33):3876-84
19817048
J Am Chem Soc. 2014 May 21;136(20):7327-32
24624980
Annu Rev Neurosci. 2014;37:435-56
25002277
Science. 2003 Aug 29;301(5637):1221-3
12947193
Nano Lett. 2006 Oct;6(10):2362-7
17034112
J Am Chem Soc. 2008 Jan 9;130(1):318-26
18076172
Nat Nanotechnol. 2006 Dec;1(3):173-81
18654182
Nanotechnology. 2015 Feb 27;26(8):084001
25650375
J Am Chem Soc. 2010 Jul 7;132(26):9157-64
20536142
J Am Chem Soc. 2015 Aug 12;137(31):9971-81
26181714
J Am Chem Soc. 2006 Feb 15;128(6):2135-41
16464116
J Am Chem Soc. 2006 May 24;128(20):6574-5
16704254
Phys Rev Lett. 1996 Mar 25;76(13):2302-2305
10060663