Bing Huang

Materials Chemistry · 2016–2017

A researcher in the Science corpus: 5 papers; trusts 7 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 54 citations into it; #3,657 of 11,513 by trust here, #4,164 by citations
• Computational Chemistry & Drug Discovery — 50 citations into it; #251 of 425 by trust here, #163 by citations
• Computational Theory and Mathematics — 47 citations into it
• Atomic and Molecular Physics, and Optics — 29 citations into it; #3,461 of 7,135 by trust here, #2,531 by citations
• Physical and Theoretical Chemistry — 26 citations into it; #193 of 671 by trust here, #51 by citations
• AI for Physical Sciences — 19 citations into it

Whom Bing Huang trusts overall

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

1. Richard M. Martin
2. Matthias Rupp 
3. R. Ramakrishnan
4. O. V. Lilienfeld
5. O. A. von Lilienfeld
6. A. P. Bart'ok
7. Felix A. Faber
8. J. Behler
9. von Lilienfeld
10. Kristof T. Schütt

Who trusts Bing Huang overall

1. Justin Gilmer
2. Sam Schoenholz
3. Oliver T. Unke
4. O. V. Lilienfeld
5. Felix A. Faber
6. Benjamín Sánchez-Lengeling
7. A. P. Bart'ok
8. Pavlo O. Dral
9. Johannes Klicpera
10. Stefan Chmiela

Papers from Bing Huang's perspective

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

1. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
2. Fast and accurate modeling of molecular atomization energies with machine learning.
3. Convolutional Networks on Graphs for Learning Molecular Fingerprints
4. Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
5. Gated Graph Sequence Neural Networks
6. Chemical space
7. Handbook of Molecular Descriptors
8. Unified Representation for Machine Learning of Molecules and Crystals
9. Quantum chemistry structures and properties of 134 kilo molecules
10. Self-Consistent Equations Including Exchange and Correlation Effects

Their papers

• Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. (2017) — 593 citations
• Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. (2016) — 280 citations
• The "DNA" of chemistry: Scalable quantum machine learning with "amons" (2017) — 38 citations
• Alchemical and structural distribution based representation for universal quantum machine learning. (2017) — 323 citations
• Machine learning prediction errors better than DFT accuracy (2017) — 36 citations