Articles with public access mandates - Rampi RamprasadLearn more
Not available anywhere: 5
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
Mandates: US Department of Defense
Effect of Fluorine in Redesigning Energy-Storage Properties of High-Temperature Dielectric Polymers
AA Deshmukh, C Wu, O Yassin, L Chen, S Shukla, J Zhou, AR Khomane, ...
ACS Applied Materials & Interfaces 15 (40), 46840-46848, 2023
Mandates: US Department of Defense
Durability Of Lanthanum Strontium Cobaltferrite ((La0.60Sr0.40)0.95(Co0.20Fe0.80)O3‐x) Cathodes In CO2 And H2O Containingair
B Hu, M K. Mahapatra, V Sharma, R Ramprasad, N Minh, S Misture, ...
Advances in Solid Oxide Fuel Cells and Electronic Ceramics: A Collection of …, 2015
Mandates: US Department of Energy
Concentric spherical neural network for 3d representation learning
J Fox, B Zhao, BG Del Rio, S Rajamanickam, R Ramprasad, L Song
2022 international joint conference on neural networks (IJCNN), 1-8, 2022
Mandates: US National Science Foundation, US Department of Energy
Accuracy of classical force fields for polyethylene structures away from equilibrium
KG Frawley, L Chen, H Tran, NN Thadhani, R Ramprasad
MRS Communications 14 (1), 1-7, 2024
Mandates: US Department of Defense
Available somewhere: 130
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 force fields: construction, validation, and outlook
V Botu, R Batra, J Chapman, R Ramprasad
The Journal of Physical Chemistry C 121 (1), 511-522, 2017
Mandates: 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 in materials science: Recent progress and emerging applications
T Mueller, AG Kusne, R Ramprasad
Reviews in computational chemistry 29, 186-273, 2016
Mandates: US National Science Foundation
Mesoporous MoO3–x Material as an Efficient Electrocatalyst for Hydrogen Evolution Reactions
Z Luo, R Miao, TD Huan, IM Mosa, AS Poyraz, W Zhong, JE Cloud, ...
Advanced Energy Materials 6 (16), 1600528, 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 (1), 1-10, 2016
Mandates: US Department of Energy
Physically informed artificial neural networks for atomistic modeling of materials
GPP Pun, R Batra, R Ramprasad, Y Mishin
Nature communications 10 (1), 2339, 2019
Mandates: US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense
Emerging materials intelligence ecosystems propelled by machine learning
R Batra, L Song, R Ramprasad
Nature Reviews Materials 6 (8), 655-678, 2021
Mandates: US National Science Foundation, US Department of Energy, US Department of …
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
Mandates: US Department of Energy
A universal strategy for the creation of machine learning-based atomistic force fields
TD Huan, R Batra, J Chapman, S Krishnan, L Chen, R Ramprasad
NPJ Computational Materials 3 (1), 37, 2017
Mandates: US National Science Foundation, US Department of Defense
Flexible temperature‐invariant polymer dielectrics with large bandgap
C Wu, AA Deshmukh, Z Li, L Chen, A Alamri, Y Wang, R Ramprasad, ...
Advanced Materials 32 (21), 2000499, 2020
Mandates: US Department of Defense
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
Factors favoring ferroelectricity in hafnia: A first-principles computational study
R Batra, TD Huan, JL Jones, G Rossetti Jr, R Ramprasad
The Journal of Physical Chemistry C 121 (8), 4139-4145, 2017
Mandates: US National Science Foundation, US Department of Defense
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