| Machine learning in materials informatics: recent applications and prospects R Ramprasad, R Batra, G Pilania, A Mannodi-Kanakkithodi, C Kim npj Computational Materials 3 (1), 54, 2017 | 1425 | 2017 |
| 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 | 407 | 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 | 289 | 2019 |
| From organized high-throughput data to phenomenological theory using machine learning: the example of dielectric breakdown C Kim, G Pilania, R Ramprasad Chemistry of Materials 28 (5), 1304-1311, 2016 | 238 | 2016 |
| Polymer informatics: Current status and critical next steps L Chen, G Pilania, R Batra, TD Huan, C Kim, C Kuenneth, R Ramprasad Materials Science and Engineering: R: Reports 144, 100595, 2021 | 217 | 2021 |
| A hybrid organic-inorganic perovskite dataset C Kim, TD Huan, S Krishnan, R Ramprasad Scientific data 4 (1), 1-11, 2017 | 205 | 2017 |
| Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites C Kim, G Pilania, R Ramprasad The Journal of Physical Chemistry C 120 (27), 14575-14580, 2016 | 205 | 2016 |
| Polymer design using genetic algorithm and machine learning C Kim, R Batra, L Chen, H Tran, R Ramprasad Computational Materials Science 186, 110067, 2021 | 204 | 2021 |
| 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 | 203 | 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 | 202 | 2020 |
| 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 | 201 | 2018 |
| A polymer dataset for accelerated property prediction and design TD Huan, A Mannodi-Kanakkithodi, C Kim, V Sharma, G Pilania, ... Scientific data 3 (1), 1-10, 2016 | 200 | 2016 |
| Finding new perovskite halides via machine learning G Pilania, PV Balachandran, C Kim, T Lookman Frontiers in Materials 3, 19, 2016 | 194 | 2016 |
| Machine learning models for the lattice thermal conductivity prediction of inorganic materials L Chen, H Tran, R Batra, C Kim, R Ramprasad Computational Materials Science 170, 109155, 2019 | 131 | 2019 |
| Frequency-dependent dielectric constant prediction of polymers using machine learning L Chen, C Kim, R Batra, JP Lightstone, C Wu, Z Li, AA Deshmukh, ... npj Computational Materials 6 (1), 61, 2020 | 127 | 2020 |
| 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 | 127 | 2019 |
| 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 | 122 | 2019 |
| Polymer informatics with multi-task learning C Kuenneth, AC Rajan, H Tran, L Chen, C Kim, R Ramprasad Patterns 2 (4), 2021 | 103 | 2021 |
| 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 | 100 | 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 | 82 | 2020 |
Huan TranGeorgia Institute of Technology & Matmerize, Inc.Vahvistettu sähköpostiosoite verkkotunnuksessa mse.gatech.edu