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 | 110 | 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 | 84 | 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 | 77 | 2017 |
Dynamic crosslinking compatibilizes immiscible mixed plastics RW Clarke, T Sandmeier, KA Franklin, D Reich, X Zhang, N Vengallur, ... Nature 616 (7958), 731-739, 2023 | 66 | 2023 |
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 | 61 | 2018 |
Data-driven methods for accelerating polymer design TK Patra ACS Polymers Au 2 (1), 8-26, 2021 | 60 | 2021 |
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 | 55 | 2019 |
Coarse-grain molecular dynamics simulations of nanoparticle-polymer melt: Dispersion vs. agglomeration TK Patra, JK Singh The Journal of Chemical Physics 138 (14), 2013 | 53 | 2013 |
Polymer directed aggregation and dispersion of anisotropic nanoparticles TK Patra, JK Singh Soft Matter 10 (11), 1823-1830, 2014 | 34 | 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 | 33 | 2020 |
Accelerating copolymer inverse design using monte carlo tree search TK Patra, TD Loeffler, SKRS Sankaranarayanan Nanoscale 12 (46), 23653-23662, 2020 | 31 | 2020 |
dPOLY: deep learning of polymer phases and phase transition D Bhattacharya, TK Patra Macromolecules 54 (7), 3065-3074, 2021 | 30 | 2021 |
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 | 28 | 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 | 27 | 2019 |
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 | 21 | 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 | 18 | 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 | 14 | 2021 |
Active learning a coarse-grained neural network model for bulk water from sparse training data TD Loeffler, TK Patra, H Chan, SKRS Sankaranarayanan Molecular Systems Design & Engineering 5 (5), 902-910, 2020 | 14 | 2020 |
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 | 13 | 2011 |
Forecasting the experimental glass transition from short time relaxation data JH Hung, TK Patra, DS Simmons Journal of Non-Crystalline Solids 544, 120205, 2020 | 12 | 2020 |