Polymer genome: a data-powered polymer informatics platform for property predictions C Kim, A Chandrasekaran, TD Huan, D Das, R Ramprasad
The Journal of Physical Chemistry C 122 (31), 17575-17585, 2018
325 2018 Solving the electronic structure problem with machine learning A Chandrasekaran, D Kamal, R Batra, C Kim, L Chen, R Ramprasad
npj Computational Materials 5 (1), 22, 2019
241 2019 Ferroelectricity, antiferroelectricity, and ultrathin 2D electron/hole gas in multifunctional monolayer MXene A Chandrasekaran, A Mishra, AK Singh
Nano letters 17 (5), 3290-3296, 2017
212 2017 Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond A Mannodi-Kanakkithodi, A Chandrasekaran, C Kim, TD Huan, G Pilania, ...
Materials Today 21 (7), 785-796, 2018
172 2018 Critical assessment of the Hildebrand and Hansen solubility parameters for polymers S Venkatram, C Kim, A Chandrasekaran, R Ramprasad
Journal of chemical information and modeling 59 (10), 4188-4194, 2019
154 2019 Machine-learning predictions of polymer properties with Polymer Genome H Doan Tran, C Kim, L Chen, A Chandrasekaran, R Batra, S Venkatram, ...
Journal of Applied Physics 128 (17), 2020
141 2020 Defect ordering and defect–domain-wall interactions in PbTiO : A first-principles study A Chandrasekaran, D Damjanovic, N Setter, N Marzari
Physical Review B 88 (21), 214116, 2013
123 2013 Active-learning and materials design: the example of high glass transition temperature polymers C Kim, A Chandrasekaran, A Jha, R Ramprasad
Mrs Communications 9 (3), 860-866, 2019
100 2019 Electrochemical stability window of polymeric electrolytes L Chen, S Venkatram, C Kim, R Batra, A Chandrasekaran, R Ramprasad
Chemistry of Materials 31 (12), 4598-4604, 2019
99 2019 Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures A Jha, A Chandrasekaran, C Kim, R Ramprasad
Modelling and Simulation in Materials Science and Engineering 27 (2), 024002, 2019
85 2019 A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap A Patra, R Batra, A Chandrasekaran, C Kim, TD Huan, R Ramprasad
Computational Materials Science 172, 109286, 2020
62 2020 A deep learning solvent-selection paradigm powered by a massive solvent/nonsolvent database for polymers A Chandrasekaran, C Kim, S Venkatram, R Ramprasad
Macromolecules 53 (12), 4764-4769, 2020
50 2020 General atomic neighborhood fingerprint for machine learning-based methods R Batra, HD Tran, C Kim, J Chapman, L Chen, A Chandrasekaran, ...
The Journal of Physical Chemistry C 123 (25), 15859-15866, 2019
44 2019 Polymer genome–based prediction of gas permeabilities in polymers G Zhu, C Kim, A Chandrasekarn, JD Everett, R Ramprasad, RP Lively
Journal of Polymer Engineering 40 (6), 451-457, 2020
40 2020 Asymmetric structure of domain walls and interactions with defects in A Chandrasekaran, XK Wei, L Feigl, D Damjanovic, N Setter, N Marzari
Physical Review B 93 (14), 144102, 2016
29 2016 Effect of crystallinity on Li adsorption in polyethylene oxide D Das, A Chandrasekaran, S Venkatram, R Ramprasad
Chemistry of Materials 30 (24), 8804-8810, 2018
27 2018 A charge density prediction model for hydrocarbons using deep neural networks D Kamal, A Chandrasekaran, R Batra, R Ramprasad
Machine Learning: Science and Technology 1 (2), 025003, 2020
23 2020 Iterative-learning strategy for the development of application-specific atomistic force fields TD Huan, R Batra, J Chapman, C Kim, A Chandrasekaran, R Ramprasad
The Journal of Physical Chemistry C 123 (34), 20715-20722, 2019
21 2019 Active learning accelerates design and optimization of hole-transporting materials for organic electronics H Abroshan, HS Kwak, Y An, C Brown, A Chandrasekaran, P Winget, ...
Frontiers in Chemistry 9, 800371, 2022
16 2022 Solving the electronic structure problem with machine learning. npj Computational Materials, 5: 22 A Chandrasekaran, D Kamal, R Batra, C Kim, L Chen, R Ramprasad
Feburary, 2019
12 2019