Simon J. L. Billinge
Materials Chemistry · 2000–2021
A researcher in the Science corpus: 15 papers; trusts 2 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 144 citations into it; #2,775 of 11,513 by trust here, #3,158 by citations
- Atomic and Molecular Physics, and Optics — 18 citations into it; #1,402 of 7,135 by trust here, #2,921 by citations
- Biomedical Engineering — 14 citations into it; #1,751 of 10,734 by trust here, #4,472 by citations
- Statistical and Nonlinear Physics — 6 citations into it; #2,515 of 10,127 by trust here, #2,370 by citations
Whom Simon J. L. Billinge trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Simon J. L. Billinge overall
- Peter J. Chupas
- Pavol Juhas
- T. Proffen
- T. Egami
- B. Ingham
- Alan K. Soper
- Karena W. Chapman
- P. Lee
- Christopher Lyn Farrow
- Timur Davis
Papers from Simon J. L. Billinge's perspective
Ordered by how strongly the researchers Simon J. L. Billinge trusts endorse each paper. Same corpus, a different order for every researcher.
- Underneath the Bragg Peaks: Structural Analysis of Complex Materials
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
- Deep Learning
- Improving neural networks by preventing co-adaptation of feature detectors
- Molecular segregation observed in a concentrated alcohol–water solution (Nature)
- PDFgetN: a user‐friendly program to extract the total scattering structure factor and the pair distribution function from neutron powder diffraction data
- Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Their papers
- PDFfit2 and PDFgui: computer programs for studying nanostructure in crystals (2007) — 1,728 citations
- Autonomous experimentation systems for materials development: A community perspective (2021) — 311 citations
- Underneath the Bragg Peaks: Structural Analysis of Complex Materials (2003) — 1,472 citations
- The Problem with Determining Atomic Structure at the Nanoscale (2007) — 641 citations
- Rapid acquisition pair distribution function (RA-PDF) analysis. (2003) — 456 citations
- Local structure from diffraction (2002) — 151 citations
- PDFgetX2: a GUI-driven program to obtain the pair distribution function from X-ray powder diffraction data (2004) — 550 citations
- Structure of nanocrystalline materials using atomic pair distribution function analysis: Study of LiMoS2 (2001) — 157 citations
- Quantitative size-dependent structure and strain determination of CdSe nanoparticles using atomic pair distribution function analysis (2007) — 166 citations
- PDFgetN: a user‐friendly program to extract the total scattering structure factor and the pair distribution function from neutron powder diffraction data (2000) — 209 citations
- Structural analysis of complex materials using the atomic pair distribution function — a practical guide (2003) — 278 citations
- Recent advances and applications of deep learning methods in materials science (2021) — 995 citations
- Two-Step Nucleation and Growth of InP Quantum Dots via Magic-Sized Cluster Intermediates (2015) — 251 citations
- PDFgetX3: a rapid and highly automatable program for processing powder diffraction data into total scattering pair distribution functions (2012) — 812 citations
- Powder diffraction : theory and practice (2008) — 271 citations