Tarak Patra
Cited by
Cited by
Neural-network-biased genetic algorithms for materials design: evolutionary algorithms that learn
TK Patra, V Meenakshisundaram, JH Hung, DS Simmons
ACS combinatorial science 19 (2), 96-107, 2017
Defect Dynamics in 2-D MoS2 Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy
TK Patra, F Zhang, DS Schulman, H Chan, MJ Cherukara, M Terrones, ...
ACS nano 12 (8), 8006-8016, 2018
Designing sequence-specific copolymer compatibilizers using a molecular-dynamics-simulation-based genetic algorithm
V Meenakshisundaram, JH Hung, TK Patra, DS Simmons
Macromolecules 50 (3), 1155-1166, 2017
Design rules for highly conductive polymeric ionic liquids from molecular dynamics simulations
Y Cheng, J Yang, JH Hung, TK Patra, DS Simmons
Macromolecules 51 (17), 6630-6644, 2018
Coarse-grain molecular dynamics simulations of nanoparticle-polymer melt: Dispersion vs. agglomeration
TK Patra, JK Singh
The Journal of Chemical Physics 138 (14), 2013
Universal localization transition accompanying glass formation: Insights from efficient molecular dynamics simulations of diverse supercooled liquids
JH Hung, TK Patra, V Meenakshisundaram, JH Mangalara, DS Simmons
Soft Matter 15 (6), 1223-1242, 2019
Data-driven methods for accelerating polymer design
TK Patra
ACS Polymers Au 2 (1), 8-26, 2021
Polymer directed aggregation and dispersion of anisotropic nanoparticles
TK Patra, JK Singh
Soft Matter 10 (11), 1823-1830, 2014
Active learning the potential energy landscape for water clusters from sparse training data
TD Loeffler, TK Patra, H Chan, M Cherukara, SKRS Sankaranarayanan
The Journal of Physical Chemistry C 124 (8), 4907-4916, 2020
Accelerating copolymer inverse design using monte carlo tree search
TK Patra, TD Loeffler, SKRS Sankaranarayanan
Nanoscale 12 (46), 23653-23662, 2020
Dynamic crosslinking compatibilizes immiscible mixed plastics
RW Clarke, T Sandmeier, KA Franklin, D Reich, X Zhang, N Vengallur, ...
Nature 616 (7958), 731-739, 2023
Active learning a neural network model for gold clusters & bulk from sparse first principles training data
TD Loeffler, S Manna, TK Patra, H Chan, B Narayanan, ...
ChemCatChem 12 (19), 4796-4806, 2020
Slippery and wear-resistant surfaces enabled by interface engineered graphene
N Dwivedi, T Patra, JB Lee, RJ Yeo, S Srinivasan, T Dutta, K Sasikumar, ...
Nano letters 20 (2), 905-917, 2019
Surface electrophoresis of ds-DNA across orthogonal pair of surfaces
A Ghosh, TK Patra, R Kant, RK Singh, JK Singh, S Bhattacharya
Applied Physics Letters 98 (16), 2011
dPOLY: Deep learning of polymer phases and phase transition
D Bhattacharya, TK Patra
Macromolecules 54 (7), 3065-3074, 2021
Ligand dynamics control structure, elasticity, and high-pressure behavior of nanoparticle superlattices
TK Patra, H Chan, P Podsiadlo, EV Shevchenko, ...
Nanoscale 11 (22), 10655-10666, 2019
A coarse-grained deep neural network model for liquid water
TK Patra, TD Loeffler, H Chan, MJ Cherukara, B Narayanan, ...
Applied Physics Letters 115 (19), 2019
Reinforcement learning in discrete action space applied to inverse defect design
TD Loeffler, S Banik, TK Patra, M Sternberg, SKRS Sankaranarayanan
Journal of Physics Communications 5 (3), 031001, 2021
Understanding adsorption behavior of silica nanoparticles over a cellulose surface in an aqueous medium
P Katiyar, TK Patra, JK Singh, D Sarkar, A Pramanik
Chemical Engineering Science 141, 293-303, 2016
Vapor-liquid phase coexistence and transport properties of two-dimensional oligomers
TK Patra, A Hens, JK Singh
The Journal of Chemical Physics 137 (8), 2012
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Articles 1–20