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 enables 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.