Rampi Ramprasad
Materials Chemistry · 2004–2020
A researcher in the Science corpus: 20 papers; trusts 73 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 325 citations into it; #600 of 11,513 by trust here, #1,783 by citations
- Electrical and Electronic Engineering — 111 citations into it; #12,897 of 16,539 by trust here, #3,551 by citations
- Computational Theory and Mathematics — 90 citations into it; #4,190 of 12,182 by trust here, #925 by citations
- Atomic and Molecular Physics, and Optics — 83 citations into it; #1,647 of 7,135 by trust here, #2,105 by citations
- Computational Chemistry & Drug Discovery — 69 citations into it
- Molecular Biology — 53 citations into it; #4,848 of 17,773 by trust here, #9,367 by citations
- Artificial Intelligence — 49 citations into it
- Physical and Theoretical Chemistry — 45 citations into it
Whom Rampi Ramprasad trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Behler
- Matthias Rupp
- V. Botu
- Radford M. Neal
- A. P. Bart'ok
- Trevor J. Hastie
- Tim Mueller
- Ghanshyam Pilania
- Klaus Nordhausen
- Michael D. Schmidt
Who trusts Rampi Ramprasad overall
- Yuri Mishin
- Rohit Batra
- Dane Morgan
- G. P. P. Pun
- Qizhi He
- Tânia F Cova
- Yu-Hang Tang
- Evgeny V. Podryabinkin
- Ryan Jacobs
- A. Mannodi‐Kanakkithodi
Papers from Rampi Ramprasad's perspective
Ordered by how strongly the researchers Rampi Ramprasad trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Physically informed artificial neural networks for atomistic modeling of materials (2018) — 333 citations
- Learning scheme to predict atomic forces and accelerate materials simulations (2015) — 183 citations
- Adaptive machine learning framework to accelerate ab initio molecular dynamics (2015) — 374 citations
- Machine Learning in Materials Science (2016) — 337 citations
- Machine Learning Force Fields: Construction, Validation, and Outlook (2016) — 485 citations
- Rational design of all organic polymer dielectrics (2014) — 303 citations
- Machine Learning Strategy for Accelerated Design of Polymer Dielectrics (2016) — 360 citations
- Machine learning bandgaps of double perovskites (2016) — 448 citations
- Factors Favoring Ferroelectricity in Hafnia: A First-Principles Computational Study (2017) — 203 citations
- Pathways towards ferroelectricity in hafnia (2014) — 345 citations
- A universal strategy for the creation of machine learning-based atomistic force fields (2017) — 224 citations
- Mesoporous MoO3–x Material as an Efficient Electrocatalyst for Hydrogen Evolution Reactions (2016) — 406 citations
- Machine Learning and Materials Informatics: Recent Applications and Prospects (2017) — 708 citations
- Stabilization of metastable phases in hafnia owing to surface energy effects (2016) — 146 citations
- Emerging materials intelligence ecosystems propelled by machine learning (2020) — 336 citations
- Frequency-dependent dielectric constant prediction of polymers using machine learning (2020) — 156 citations
- Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions (2018) — 444 citations
- Dopants Promoting Ferroelectricity in Hafnia: Insights from a comprehensive Chemical Space Exploration (2017) — 188 citations
- Advanced polymeric dielectrics for high energy density applications (2016) — 328 citations
- Magnetic properties of metallic ferromagnetic nanoparticle composites (2004) — 162 citations