Rohit Batra

Materials Chemistry · 2016–2020

A researcher in the Science corpus: 9 papers; trusts 55 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 229 citations into it; #3,764 of 11,513 by trust here, #3,905 by citations
• Electrical and Electronic Engineering — 74 citations into it
• Computational Theory and Mathematics — 69 citations into it; #11,037 of 12,182 by trust here, #2,015 by citations
• Atomic and Molecular Physics, and Optics — 52 citations into it; #5,546 of 7,135 by trust here, #2,921 by citations
• Computational Chemistry & Drug Discovery — 42 citations into it
• Molecular Biology — 38 citations into it
• AI for Physical Sciences — 24 citations into it
• Artificial Intelligence — 24 citations into it

Whom Rohit Batra trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. J. Behler
2. Connor W. Coley
3. V. Botu
4. A. P. Bart'ok
5. Matthias Rupp 
6. Marwin H. S. Segler
7. Rampi Ramprasad
8. Kristof T. Schütt
9. Stefan Chmiela
10. Benjamín Sánchez-Lengeling

Who trusts Rohit Batra overall

1. Yuri Mishin
2. Nour Makke
3. Dane Morgan
4. Sanjay Chawla
5. An Chen
6. Qizhi He
7. Alireza Kashir
8. E. Grimley
9. Rampi Ramprasad

Papers from Rohit Batra's perspective

Ordered by how strongly the researchers Rohit Batra trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Physically informed artificial neural networks for atomistic modeling of materials (2018) — 333 citations
• Machine Learning Force Fields: Construction, Validation, and Outlook (2016) — 485 citations
• Factors Favoring Ferroelectricity in Hafnia: A First-Principles Computational Study (2017) — 203 citations
• A universal strategy for the creation of machine learning-based atomistic force fields (2017) — 224 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
• Dopants Promoting Ferroelectricity in Hafnia: Insights from a comprehensive Chemical Space Exploration (2017) — 188 citations