ORIGINAL RESEARCH article
Front. Chem.
Sec. Theoretical and Computational Chemistry
Machine Learning Approach to Topological Graph Descriptors of Graphene Nanoribbons
Provisionally accepted
- 1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India
- 2Department of Biotechnology, Rathinam College of Arts and Science, Coimbatore, India
- 3Department of Mathematics, Nirmala College, Muvattupuzha, India
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Abstract Bottom-up syntheses of graphene nanoribbons have gathered considerable research interest because of their electronic properties and quantum behaviors which enhance their significance in nanotechnology. The advancement of material's design methods and applications heavily depends on understanding how structural topology influences functional properties. This study analyzed valency based molecular descriptors of graphene nanoribbons with machine learning techniques. We have computed various valency based molecular descriptors of graphene nanoribbons and studied their predictive power using the logistic regression machine learning technique by describing its receiver operating characteristic curve to analyze the topological features of these graphene nanoribbons. These descriptors represent quantitative measurements of crucial structural features of graphene nanoribbons that directly affect material properties. This predictive framework en-ables researchers to design graphene nanoribbons with specific functionalities while advancing their knowledge about structure-property relationships in this material class. Molecular descriptors combined with machine learning methods demonstrate the potential to accelerate the discovery process and optimization of advanced nanomaterials.
Keywords: Degree based topological descriptors, graphene nanoribbons, machine learning, machine learning, Regression Analysis
Received: 20 Nov 2025; Accepted: 29 Jan 2026.
Copyright: © 2026 K, S, Sekar Ponnusamy, K B, Vijay J and Augustine. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Roy S
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