Book Chapter

• Patra T K, Khan S, Srivastava R and Singh J K; Understanding wetting transitions using molecular simulations, Nanoscale and Microscale Phenomena, Edited by Joshi Y M and Khandekar S, Springer 2015 (ISBN 978-81-322-2288-0)

Journal Publications

2022

• Himanshu and Patra T. K., When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate models, arXiv:2210.06622 (2022)

• Ayush K, Seth A. and Patra T. K., nanoNET: Machine Learning Platform for Predicting Nanoparticles Distribution in a Polymer Matrix, arXiv:2208.11448 (2022)

• Ramesh P. S. and Patra, T. K., Polymer sequence design via molecular simulation-based active learning, Under review (2022)

• Gautham S. M. B. and Patra T. K., Deep learning potential of mean force between polymer grafted nanoparticles. Soft Matter 18, 7909 (2022)

• Bale, A.A., Gautham, S.M.B., Patra, T.K., Sequence-defined Pareto frontier of a copolymer structure. Journal of Polymer Science 60, 2100 (2022)

• Patra T K, Data-Driven Methods for Accelerating Polymer Design, ACS Polymers Au 2, 8 (2022)

2021

• Bhattacharya D and Patra T K, Deep learning order parameter for polymer phase transition, Preprint, arXiv:2102.12009 (2021)

• Bhattacharya D and Patra T K, dPOLY: Deep learning of polymer phases and phase transition, Macromolecules 54, 3065 (2021).

• Loeffler T D, Banik S, Patra T K, Sternberg M, Sankaranarayanan S KRS, Reinforcement learning in discrete action space applied to inverse defect design, Journal of Physics Communications (2021).

• Dwivedi N, Neogi A, Patra T K, Dhand C, Dutta T, Yeo R J, Kumar R, Hashmi S A R, Srivastava A K, Tripathy S, Saifullah M S M, Sankaranarayanan S K R S, and Bhatia C. S., Angstrom-Scale Transparent Overcoats: Interfacial Nitrogen-Driven Atomic Intermingling Promotes Lubricity and Surface Protection of Ultrathin Carbon, Nano Letters 21, 8960 (2021)

2020

• Patra T K, Loeffler T D, Sankaranarayanan S KRS, Accelerating copolymer inverse design using Monte Carlo Tree Search, Nanoscale 12, 23563 (2020)

• Manna S, Loeffler T D, Patra T K, Chan H, Narayanan B, Sankaranarayanan S KRS, Active learning a neural network model for gold clusters and bulk from sparse first principles training data, ChemCatChem 12, 4796 (2020)

• Hung J, Patra T K, and Simmons D S, Forecasting the experimental glass transition temperature from short time relaxation data, Journal of Non-Crystalline Solids 544, 120205 (2020)

• Loeffler T D, Patra T K, Chan H, Cherukara M J, Sankaranarayanan S KRS, Active learning a coarse-grained neural network model for bulk water from sparse training data, Molecular System Design and Engineering 5, 902 (2020)

• Loeffler T D, Patra T K, Chan H, Cherukara M J, Sankaranarayanan S KRS, Active learning the potential energy landscape for water clusters from sparse training data, The Journal of Physical Chemistry C 124, 4907 (2020)

• Dwivedi N, Patra T K, Lee J-B, Yeo J R, Srinivasan S, Dutta T, Sasikumar K, Dhand C, Tripathy S, Saifullah M. S. M, Danner A, Hashmi S. A. R, Srivastava A. K., Ahn J-H, Sankaranarayanan S. K. R. S., Yang H, and Bhatia C. S., Slippery and wear-resistant surfaces enabled by interface engineered graphene, Nano Letters 20, 905 (2020).

2019-2010

• Patra T K, Loeffler T D, Chan H, Cherukara M J, Narayanan B, Sankaranarayanan S KRS, A coarse-grained deep neural network model for liquid water, Applied Physics Letters 115, 193101 (2019)

• Patra T K, Chan H, Shevchenko E V, Sankaranarayanan S KRS, Narayanan B, Ligand dynamics control structure, elasticity, and high-pressure behavior of nanoparticles supercrystals, Nanoscale 11, 10655 (2019)

• Hung J, Patra T K, Meenakshisundaram V, Mangalara J H and Simmons D S, Universal localization transition underlying glass formation: insights from efficient molecular dynamics simulations of diverse supercooled liquids, Soft Matter 15, 1223 (2019)

• Cheng Y, Yang J, Hung H, Patra T K*, and Simmons D S, Design rules for highly conductive polymeric ionic liquids from molecular dynamics simulations, Macromolecules 51, 6630 (2018)

• Patra T K, Zhang F, Schulman D; Chan H, Cherukara M, Terrones M; Das S, Narayanan B, Sankaranarayanan S KRS, Defect dynamics in 2D materials probed by combining machine learning, molecular simulation and high-resolution microscopy, ACS Nano 12, 8006 (2018)

• Patra T K, Meenakshisundaram V, Hung J, and Simmons D S, Neural network biased genetic algorithm for materials design: Evolutionary algorithms that learn, ACS Combinatorial Science 19, 96 (2017)

• Meenakshisundaram V, Hung J, Patra T K Simmons D S, Designing sequence specific copolymer compatibilizers using a molecular-dynamics-simulation-based genetic algorithm, Macromolecules 50, 1155 (2017)

• Katiyar P, Patra T K, Singh J K, Sarkar D and Pramanik A, Understanding adsorption behavior of silica nanoparticles over a cellulose surface in an aqueous medium, Chemical Engineering Science 141, 293 (2016)

• Patra T K, Katiyar P and Singh J K, Substrate directed self-assembly of anisotropic nanoparticles, Chemical Engineering Science 121, 16 (2015)

• Patra T K and Singh J K, Localization and stretching of polymers at the junction of two surfaces, Journal of Chemical Physics 140, 204909 (2014)

• Patra T K and Singh J K, Polymer directed aggregation and dispersion of anisotropic nanoparticles, Soft Matter 10, 1823 (2014)

• Patra T K and Singh J K, Coarse-grain molecular dynamics simulations of nanoparticle-polymer melts: Dispersion vs. Agglomeration, Journal of Chemical Physics 138, 144901 (2013)

• Patra T K, Hens A, Singh JK, Vapor-liquid phase coexistence and transport properties of two-dimensional oligomers, Journal of Chemical Physics 137, 084701 (2012)

• Ghosh A, Patra T K, Rishikant, Singh R K, Singh J K and Bhattacharya S, Surface electrophoresis of ds-DNA across orthogonal pair of surfaces, Applied Physics Letters 98, 164102 (2011)

• B Ashok and Patra T K, Locating phase transitions in computationally hard problems, Pramana-Journal of Physics 75, 549 (2010)