ReviewChemical Society reviews2022
Trusting our machines: validating machine learning models for single-molecule transport experiments.
Review in Chemical Society reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Machine Learning-Enhanced Molecular Dynamics of 4,4'-Bipyridine in a Break Junction: A Supramolecular Origin of the High Conductance.ACS nanoscience Au · 2026Article
- Protein force spectroscopy using magnetic tweezers: Slow and steady wins the race?Biophysical journal · 2026Review
- Precise Probing of Interfaces at the Single-Molecule Scale.Nanomaterials (Basel, Switzerland) · 2026Review
- Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges.Precision chemistry · 2024Review
- Single-molecule detection of modified amino acid regulating transcriptional activity.RSC advances · 2024Article
- Making the Most of Nothing: One-Class Classification for Single-Molecule Transport Studies.ACS nanoscience Au · 2024Article
- Methods for the analysis, interpretation, and prediction of single-molecule junction conductance behaviour.Chemical science · 2024Review
- Total variation denoising-based method of identifying the states of single molecules in break junction data.Discover nano · 2024Article
- Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method.The journal of physical chemistry letters · 2023Article
- Direct biomolecule discrimination in mixed samples using nanogap-based single-molecule electrical measurement.Scientific reports · 2023Article
- Quantum Interference and Contact Effects in the Thermoelectric Performance of Anthracene-Based Molecules.The journal of physical chemistry. C, Nanomaterials and interfaces · 2023Article
- An artificial synapse based on molecular junctions.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically single-molecule electron transport experiments, but the concepts and problems we explore will be sufficiently general for application in other fields with similar data. In the first part of the tutorial review, we will introduce the field of single-molecule transport, and provide an overview of the most common machine learning algorithms employed. In the second part of the tutorial review, we will show, through examples grounded in single-molecule transport, that the promises of machine learning can only be fulfilled by careful application. We will end the tutorial review with a discussion of where we, as a field, could go from here.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.