Articles with public access mandates - Ghanshyam PilaniaLearn more
Not available anywhere: 2
Machine learning for melting temperature predictions and design in polyhydroxyalkanoate-based biopolymers
KK Bejagam, J Lalonde, CN Iverson, BL Marrone, G Pilania
The Journal of Physical Chemistry B 126 (4), 934-945, 2022
Mandates: US Department of Energy
A first-principles investigation of nitrogen reduction to ammonia on zirconium nitride and oxynitride surfaces
A Banerjee, BM Ceballos, C Kreller, R Mukundan, G Pilania
Journal of Materials Science 57 (22), 10213-10224, 2022
Mandates: US Department of Energy
Recused: 1
High-Dielectric 3-D Printable Materials for Laser Accelerators
EM Walker, RD Gilbertson, EI Simakov, G Pilania, RE Muenchausen
2018 IEEE Advanced Accelerator Concepts Workshop (AAC), 1-5, 2018
Mandates: US Department of Energy
Responsible authors: EM Walker
Available somewhere: 77
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
Mandates: US Department of Energy, US Department of Defense
Machine learning bandgaps of double perovskites
G Pilania, A Mannodi-Kanakkithodi, BP Uberuaga, R Ramprasad, ...
Scientific reports 6 (1), 19375, 2016
Mandates: US Department of Energy
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
Mandates: US Department of Energy
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
Mandates: US National Science Foundation, US Department of Energy
Multi-fidelity machine learning models for accurate bandgap predictions of solids
G Pilania, JE Gubernatis, T Lookman
Computational Materials Science 129, 156-163, 2017
Mandates: US Department of Energy
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
Mandates: US Department of Energy
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
Mandates: US National Science Foundation, US Department of Energy, US Department of …
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
Mandates: US Department of Energy
Machine Learning in Materials Science: From Explainable Predictions to Autonomous Design
G Pilania
Computational Materials Science 193, 110360, 2021
Mandates: US Department of Energy
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
Mandates: US Department of Energy, US Department of Defense
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
Mandates: US National Science Foundation, US Department of Energy
Finding new perovskite halides via machine learning
G Pilania, PV Balachandran, C Kim, T Lookman
Frontiers in Materials 3, 19, 2016
Mandates: US Department of Energy
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
Mandates: US Department of Energy
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
Mandates: US Department of Energy
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
Mandates: US Department of Energy
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
Mandates: US Department of Energy, National Natural Science Foundation of China …
Machine learning for materials design and discovery
R Vasudevan, G Pilania, PV Balachandran
Journal of Applied Physics 129 (7), 2021
Mandates: US Department of Energy
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