30 citations · 50 across the 9 of their papers we have counts for
8 papers
When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate models
Himanshu, Tarak K Patra
Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction…
Sequence Engineering of Copolymers using Evolutionary Computing
Ashwin A Bale, Tarak K Patra
The correlations between the sequence of monomers in a polymer and its three-dimensional structure is a grand challenge in polymer science and biology. The properties and functions…
Deep Learning Order Parameter for Polymer Phase Transition
Debjyoti Bhattacharya, Tarak K Patra
We report a deep learning (DL) framework viz. deep autoencoder that autonomously discovers an appropriate order parameter from molecular dynamics (MD) simulation data to characteri…
dPOLY: Deep Learning of Polymer Phases and Phase Transition
Debjyoti Bhattacharya, Tarak K Patra
Machine learning (ML) and artificial intelligence (AI) have the remarkable ability to classify, recognize, and characterize complex patterns and trends in large data sets. Here, we…
Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data
Troy D Loeffler, Sukriti Manna, Tarak K Patra +3
Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs d…
Accelerating Copolymer Inverse Design using AI Gaming algorithm
Tarak K Patra, Troy D. Loeffler, Subramanian K R S Sankaranarayanan
There exists a broad class of sequencing problems, for example, in proteins and polymers that can be formulated as a heuristic search algorithm that involve decision making akin to…