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 | 992 | 2017 |
Accelerating materials property predictions using machine learning G Pilania, C Wang, X Jiang, S Rajasekaran, R Ramprasad Scientific reports 3 (1), 1-6, 2013 | 687 | 2013 |
Machine learning bandgaps of double perovskites G Pilania, A Mannodi-Kanakkithodi, BP Uberuaga, R Ramprasad, ... Scientific reports 6 (1), 19375, 2016 | 394 | 2016 |
Machine Learning Strategy for Accelerated Design of Polymer Dielectrics A Mannodi-Kanakkithodi, G Pilania, TD Huan, T Lookman, R Ramprasad Scientific Reports 6, 20952, 2016 | 312 | 2016 |
Rational design of all organic polymer dielectrics V Sharma, C Wang, RG Lorenzini, R Ma, Q Zhu, DW Sinkovits, G Pilania, ... Nature communications 5 (1), 4845, 2014 | 273 | 2014 |
Multi-fidelity machine learning models for accurate bandgap predictions of solids G Pilania, JE Gubernatis, T Lookman Computational Materials Science 129, 156-163, 2017 | 254 | 2017 |
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown CK Kim, G Pilania, R Ramprasad Chemistry of Materials, 2016 | 198 | 2016 |
A polymer dataset for accelerated property prediction and design TD Huan, A Mannodi-Kanakkithodi, C Kim, V Sharma, G Pilania, ... Scientific Data 3, 160012, 2016 | 157 | 2016 |
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design K Choudhary, KF Garrity, ACE Reid, B DeCost, AJ Biacchi, ... npj computational materials 6 (1), 173, 2020 | 153 | 2020 |
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 | 148 | 2016 |
Finding new perovskite halides via machine learning G Pilania, PV Balachandran, C Kim, T Lookman Frontiers in Materials 3, 19, 2016 | 148 | 2016 |
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, 2018 | 143 | 2018 |
Computational strategies for polymer dielectrics design CC Wang, G Pilania, SA Boggs, S Kumar, C Breneman, R Ramprasad Polymer 55 (4), 979-988, 2014 | 134 | 2014 |
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 | 104 | 2021 |
Revisiting the Al/Al2O3 Interface: Coherent Interfaces and Misfit Accommodation G Pilania, BJ Thijsse, RG Hoagland, I Lazić, SM Valone, XY Liu Scientific reports 4 (1), 4485, 2014 | 97 | 2014 |
Machine Learning in Materials Science: From Explainable Predictions to Autonomous Design G Pilania Computational Materials Science 193, 110360, 2021 | 80 | 2021 |
Machine-learning-based predictive modeling of glass transition temperatures: A case of polyhydroxyalkanoate homopolymers and copolymers G Pilania, CN Iverson, T Lookman, BL Marrone Journal of Chemical Information and Modeling 59 (12), 5013-5025, 2019 | 76 | 2019 |
Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning H Zong, G Pilania, X Ding, GJ Ackland, T Lookman npj Computational Materials 4 (1), 48, 2018 | 75 | 2018 |
Ab initio study of ferroelectricity in BaTiO 3 nanowires G Pilania, SP Alpay, R Ramprasad Physical Review B 80 (1), 014113, 2009 | 74 | 2009 |
How critical are the van der Waals interactions in polymer crystals? CS Liu, G Pilania, C Wang, R Ramprasad The Journal of Physical Chemistry A 116 (37), 9347-9352, 2012 | 70 | 2012 |