Molecular Junctions and Nanostructures
Open access
Peer reviewed
Scollr summary
What this paper is about
This study analyzed 115 observations involving molecular length, energy gap, conductance, and stability using machine learning techniques to predict molecular stability and identified challenges related to device integration, power consumption, and fault tolerance.
Full abstract
Read the full abstract
Molecular electronics is an emerging area of mesoscopic science that examines the electronic properties of molecules for advanced computing applications. This study analyzed 115 observations involving molecular length, energy gap, conductance, and stability using machine learning techniques. Strong relationships were observed among these parameters, including negative correlations between length and energy gap, positive correlations between length and conductance, and negative correlations between energy gap and conductance. Three models—Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR)—were applied to predict molecular stability. RFR performed best during training, whereas LR achieved stronger test-data generalization. The study also identified challenges related to device integration, power consumption, and fault tolerance. The multilayer edge molecular electronic device approach offers improved molecular connections and conduction control. These developments may support applications in quantum computing and biosensing. Overall, molecular electronics could enable extreme miniaturization and innovative device functions beyond conventional silicon technology, potentially becoming commercially practical within the next decade.
Direct answer
What can I do from this paper page?
Use this page to scan "Machine Learning Approaches in Molecular Electronics: A Comparative Study of Linear Regression, Random Forest Regression, and Support Vector Machines" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Molecular Junctions and Nanostructures research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Mani2026Machine,
title = {Machine Learning Approaches in Molecular Electronics: A Comparative Study of Linear Regression, Random Forest Regression, and Support Vector Machines},
author = {Sathishkumar Mani},
journal = {Journal on Electronic and Automation Engineering},
year = {2026},
doi = {10.46632/jeae/5/3/1},
url = {https://doi.org/10.46632/jeae/5/3/1}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Molecular Junctions and Nanostructures research papers?
Follow Molecular Junctions and Nanostructures research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Machine Learning Approaches in Molecular Electronics: A Comparative Study of Linear Regression, Random Forest Regression, and Support Vector Machines. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app