Rampi Ramprasad
Cited by
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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
Accelerating materials property predictions using machine learning
G Pilania, C Wang, X Jiang, S Rajasekaran, R Ramprasad
Scientific reports 3 (1), 2810, 2013
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
Machine learning bandgaps of double perovskites
G Pilania, A Mannodi-Kanakkithodi, BP Uberuaga, R Ramprasad, ...
Scientific reports 6 (1), 19375, 2016
Machine learning in materials science: Recent progress and emerging applications
T Mueller, AG Kusne, R Ramprasad
Reviews in computational chemistry 29, 186-273, 2016
Adaptive machine learning framework to accelerate ab initio molecular dynamics
V Botu, R Ramprasad
International journal of quantum chemistry 115 (16), 1074-1083, 2015
Pathways towards ferroelectricity in hafnia
TD Huan, V Sharma, GA Rossetti Jr, R Ramprasad
Physical Review B 90 (6), 064111, 2014
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
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
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
Advanced polymeric dielectrics for high energy density applications
TD Huan, S Boggs, G Teyssedre, C Laurent, M Cakmak, S Kumar, ...
Progress in Materials Science 83, 236-269, 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
Physically informed artificial neural networks for atomistic modeling of materials
GPP Pun, R Batra, R Ramprasad, Y Mishin
Nature communications 10 (1), 2339, 2019
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
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
Learning scheme to predict atomic forces and accelerate materials simulations
V Botu, R Ramprasad
Physical Review B 92 (9), 094306, 2015
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
Monolithically integrated spinel MxCo3− xO4 (M= Co, Ni, Zn) nanoarray catalysts: scalable synthesis and cation manipulation for tunable low‐temperature CH4 and CO oxidation
Z Ren, V Botu, S Wang, Y Meng, W Song, Y Guo, R Ramprasad, SL Suib, ...
Angewandte Chemie 126 (28), 7351-7355, 2014
Magnetic properties of metallic ferromagnetic nanoparticle composites
R Ramprasad, P Zurcher, M Petras, M Miller, P Renaud
Journal of applied physics 96 (1), 519-529, 2004
Emerging materials intelligence ecosystems propelled by machine learning
R Batra, L Song, R Ramprasad
Nature Reviews Materials 6 (8), 655-678, 2021
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Articles 1–20