Ghanshyam Pilania

Materials Chemistry · 2013–2020

A researcher in the Science corpus: 6 papers; trusts 11 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 107 citations into it; #3,583 of 11,513 by trust here, #3,323 by citations
• Atomic and Molecular Physics, and Optics — 30 citations into it
• Computational Theory and Mathematics — 28 citations into it; #6,612 of 12,182 by trust here, #822 by citations
• Computational Chemistry & Drug Discovery — 24 citations into it
• Artificial Intelligence — 17 citations into it
• Molecular Biology — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Statistics and Probability — 6 citations into it

Whom Ghanshyam Pilania trusts overall

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

1. Richard M. Martin
2. Matthias Rupp 
3. K. Müller
4. David Ruppert
5. Jochen Heyd
6. Luca M. Ghiringhelli
7. John C. Snyder
8. Radford M. Neal
9. Tomaso A. Poggio
10. Stefan Goedecker

Who trusts Ghanshyam Pilania overall

1. Grace X. Gu
2. Rampi Ramprasad
3. Logan T. Ward
4. A. Mannodi‐Kanakkithodi
5. Chun-Teh Chen
6. Rohit Batra
7. Tran Doan Huan
8. Chiho Kim
9. Ramin Bostanabad
10. Wojciech Samek

Papers from Ghanshyam Pilania's perspective

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

1. Gradient-based learning applied to document recognition
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Pattern Recognition and Machine Learning
4. The Elements of Statistical Learning
5. ImageNet classification with deep convolutional neural networks
6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
7. Inhomogeneous Electron Gas
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Accelerating materials property predictions using machine learning (2013) — 761 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
• Machine Learning and Materials Informatics: Recent Applications and Prospects (2017) — 708 citations
• JARVIS: An Integrated Infrastructure for Data-driven Materials Design (2020) — 371 citations