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Ghanshyam Pilania
Ghanshyam Pilania
Senior Engineer, GE Aerospace Research
Verified email at ge.com
Title
Cited by
Cited by
Year
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
13382017
Accelerating materials property predictions using machine learning
G Pilania, C Wang, X Jiang, S Rajasekaran, R Ramprasad
Scientific reports 3 (1), 2810, 2013
8332013
Machine learning bandgaps of double perovskites
G Pilania, A Mannodi-Kanakkithodi, BP Uberuaga, R Ramprasad, ...
Scientific reports 6 (1), 19375, 2016
4802016
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
3662016
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
3252020
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
3232014
Multi-fidelity machine learning models for accurate bandgap predictions of solids
G Pilania, JE Gubernatis, T Lookman
Computational Materials Science 129, 156-163, 2017
3162017
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
2362016
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
1962016
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
1942018
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
1942016
Finding new perovskite halides via machine learning
G Pilania, PV Balachandran, C Kim, T Lookman
Frontiers in Materials 3, 19, 2016
1902016
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
1892021
Machine Learning in Materials Science: From Explainable Predictions to Autonomous Design
G Pilania
Computational Materials Science 193, 110360, 2021
1812021
Computational strategies for polymer dielectrics design
CC Wang, G Pilania, SA Boggs, S Kumar, C Breneman, R Ramprasad
Polymer 55 (4), 979-988, 2014
1542014
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
1182019
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
1112014
Structure classification and melting temperature prediction in octet AB solids via machine learning
G Pilania, JE Gubernatis, T Lookman
Physical Review B 91 (21), 214302, 2015
892015
A machine learning approach for the prediction of formability and thermodynamic stability of single and double perovskite oxides
A Talapatra, BP Uberuaga, CR Stanek, G Pilania
Chemistry of Materials 33 (3), 845-858, 2021
882021
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
812018
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