Cecilia Clementi

Materials Chemistry · 2000–2021

A researcher in the Science corpus: 12 papers; trusts 60 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 185 citations into it; #2,419 of 11,513 by trust here, #4,543 by citations
• Molecular Biology — 184 citations into it; #2,132 of 17,773 by trust here, #5,405 by citations
• Computational Theory and Mathematics — 83 citations into it
• Artificial Intelligence — 78 citations into it; #26,009 of 35,345 by trust here, #25,809 by citations
• Atomic and Molecular Physics, and Optics — 78 citations into it; #1,889 of 7,135 by trust here, #2,921 by citations
• Computational Chemistry & Drug Discovery — 63 citations into it
• Statistical and Nonlinear Physics — 61 citations into it; #5,357 of 10,127 by trust here, #5,894 by citations
• Spectroscopy — 45 citations into it

Whom Cecilia Clementi trusts overall

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

1. Frank No'e
2. Feliks Nuske
3. Hao Wu
4. J. Behler
5. Radford M. Neal
6. Vijay S. Pande
7. Yann LeCun
8. Vladimir Naumovich Vapnik
9. Diederik P. Kingma
10. Jan-Hendrik Prinz

Who trusts Cecilia Clementi overall

1. Joanna I. Sulkowska
2. Yaakov Levy
3. J. Myers
4. José N. Onuchic
5. Hao Wu
6. Faruck Morcos
7. Andreas Mardt
8. Payel Das
9. Richard Bonneau
10. Marek Cieplak

Papers from Cecilia Clementi's perspective

Ordered by how strongly the researchers Cecilia Clementi 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. Gradient-based learning applied to document recognition
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Sparse learning of stochastic dynamical equations. (2017) — 279 citations
• Topological and energetic factors: what determines the structural details of the transition state ensemble and "en-route" intermediates for protein folding? An investigation for small globular proteins. (2000) — 1,186 citations
• Machine learning for molecular simulation (2019) — 819 citations
• Kinetic distance and kinetic maps from molecular dynamics simulation. (2015) — 205 citations
• Machine Learning of Coarse-Grained Molecular Dynamics Force Fields (2018) — 484 citations
• Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. (2020) — 49 citations
• Data-driven approximation of the Koopman generator: Model reduction, system identification, and control (2019) — 310 citations
• Coarse graining molecular dynamics with graph neural networks. (2020) — 219 citations
• Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction (2006) — 317 citations
• Surveying biomolecular frustration at atomic resolution (2020) — 64 citations
• Unsupervised Learning Methods for Molecular Simulation Data (2021) — 320 citations
• AWSEM-MD: Protein Structure Prediction Using Coarse-grained Physical Potentials and Bioinformatically Based Local Structure Biasing (2012) — 305 citations