There has been a tremendous increase in demand for virgin and postconsumer recycled
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …

The application of machine learning (ML) techniques to metal-based nanomaterials has
contributed greatly to understanding the interaction of nanoparticles, properties prediction …

We propose a chemical language processing model to predict polymers' glass transition
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …

Accurately predicting new polymers' properties with machine learning models apriori to
synthesis has potential to significantly accelerate new polymers' discovery and …

The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming
increasingly important as AI technology advances. The development of high‐performance …

Chitosan-based nanoparticles (CNPs) are widely used in drug delivery, cosmetics
formulation and food applications. To accelerate the manufacturing of CNPs, the present …

There has been rapidly growing demand of polymeric materials coming from different
aspects of modern life because of the highly diverse physical and chemical properties of …

In this work, we present the analytical closed forms of the Balaban index for anthracene and
catacondensed benzenoid systems using group theoretic techniques. The Balaban index is …

Abstract Machine learning (ML) is a powerful technique for extracting insights from
multivariate data quickly and efficiently. It provides a much-needed way to speed up the …

This dissertation proposes a monolithic Lagrangian meshfree solution for Fluid-Structure
Interaction (FSI) problems within the Optimal Transportation Meshfree (OTM) framework. It …