2 citations · 4 across the 6 of their papers we have counts for
6 papers
Entropic Cohesion in Vitrimers
Rahul Karmakar, Himanshu, Srikanth Sastry +2
Vitrimers are polymer networks that can undergo bond exchange reactions. They dynamically rearrange their structures while maintaining their overall integrity, thus resulting in un…
Predicting Pair Correlation Functions of Glasses using Machine Learning
Kumar Ayush, Pooja Sahu, Sk Musharaf Ali +1
Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property rel…
Explainability and Transferability of Machine Learning Models for Predicting the Glass Transition Temperature of Polymers
Agrim Babbar, Sriram Ragunathan, Debirupa Mitra +2
Machine learning offers promising tools to develop surrogate models for polymer structure-property relations. Surrogate models can be built upon existing polymer data and are usefu…
Accelerated Design of Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning
Jan Michael Y. Carrillo, Vijith P, Tarak K. Patra +5
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatbilization, chemical transformations and separations. s-BCP…
nanoNET: Machine Learning Platform for Predicting Nanoparticles Distribution in a Polymer Matrix
Kumar Ayush, Abhishek Seth, Tarak K Patra
Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal compositio…
Deep Learning Potential of Mean Force between Polymer Grafted Nanoparticles
Sachin Gautham, Tarak Patra
Grafting polymer chains on nanoparticles surfaces is a well-known route to control their self assembly and distribution in a polymer matrix. A wide variety of self assembled struct…