Automatic classification of single-molecule charge transport data with an unsupervised machine-learning algorithm

2020 ◽  
Vol 22 (3) ◽  
pp. 1674-1681 ◽  
Author(s):  
Feifei Huang ◽  
Ruihao Li ◽  
Gan Wang ◽  
Jueting Zheng ◽  
Yongxiang Tang ◽  
...  
Keyword(s):  
Charge Transport ◽  
Single Molecule ◽  
Learning Algorithm ◽  
Analysis Method ◽  
Automatic Data ◽  
Single Molecule Level ◽  
Unsupervised Deep Learning ◽  
Automatic Data Analysis ◽  
Physical And Chemical

Based on unsupervised deep learning algorithms, an automatic data analysis method for single-molecule charge transport data is developed, which offers an opportunity to reveal more physical and chemical phenomena at the single-molecule level.

Substitution Pattern Controlled Charge Transport in BN-Embedded Aromatics-Based Single-Molecule Junctions

2021 ◽  
Author(s):  
Hongxiang Li ◽  
Rui Wang ◽  
Kai Song ◽  
Caiyun Wei ◽  
Wenjing Hong ◽  
...  
Keyword(s):  
Charge Transport ◽  
Single Molecule ◽  
Molecular Devices ◽  
Substitution Pattern ◽  
Single Molecule Level ◽  
Single Molecule Junctions ◽  
Molecular Conductance

The understanding of charge transport at single-molecule level is a pre-requisite for the fabrication of molecular devices. Here, we systematically investigate the relation among molecular conductance, substitution pattern and stimuli...

scholarly journals Unsupervised vector-based classification of single-molecule charge transport data

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Mario Lemmer ◽  
Michael S. Inkpen ◽  
Katja Kornysheva ◽  
Nicholas J. Long ◽  
Tim Albrecht
Keyword(s):  
Charge Transport ◽  
Single Molecule

scholarly journals gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 211
Author(s):  
Fernando Perez-Sanz ◽  
Victoria Ruiz-Hernández ◽  
Marta I. Terry ◽  
Sara Arce-Gallego ◽  
Julia Weiss ◽  
...  
Keyword(s):  
Clock Gene ◽  
Floral Scent ◽  
R Package ◽  
Large Datasets ◽  
Wild Type ◽  
Circadian Clock Gene ◽  
Automatic Data ◽  
The Third ◽  
Automatic Data Analysis

Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites.

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