Michele Ceriotti
Materials Chemistry · 2010–2021
A researcher in the Science corpus: 15 papers; trusts 31 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 178 citations into it; #3,146 of 11,513 by trust here, #3,398 by citations
- Atomic and Molecular Physics, and Optics — 123 citations into it; #4,147 of 7,135 by trust here, #2,472 by citations
- Computational Theory and Mathematics — 71 citations into it; #6,229 of 12,182 by trust here, #622 by citations
- Molecular Biology — 66 citations into it
- Computational Chemistry & Drug Discovery — 60 citations into it; #358 of 425 by trust here, #125 by citations
- Physical and Theoretical Chemistry — 48 citations into it; #414 of 671 by trust here, #120 by citations
- AI for Physical Sciences — 32 citations into it
- Electrical and Electronic Engineering — 24 citations into it
Whom Michele Ceriotti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michele Parrinello
- Giovanni Bussi
- Kieron Burke
- Axel D. Becke
- Arthur F. Voter
- Radford M. Neal
- Roberto Car
- A. P. Bart'ok
- J. Behler
- Christopher K. I. Williams
Who trusts Michele Ceriotti overall
- Frank No'e
- Dominique C. Perrault-Joncas
- Félix Musil
- Massimiliano Bonomi
- Tim Dockhorn
- T. Wen
- Linfeng Zhang
- Jeffrey M. McMahon
- Cecilia Clementi
- Julia Westermayr
Papers from Michele Ceriotti's perspective
Ordered by how strongly the researchers Michele Ceriotti trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- The Elements of Statistical Learning
- Deep Learning
- ImageNet classification with deep convolutional neural networks
- Pattern Recognition and Machine Learning
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- Machine learning unifies the modeling of materials and molecules (2017) — 652 citations
- Comparing molecules and solids across structural and alchemical space. (2015) — 593 citations
- Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems. (2017) — 241 citations
- Gaussian Process Regression for Materials and Molecules (2021) — 1,025 citations
- Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements. (2018) — 59 citations
- Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. (2018) — 148 citations
- Promoting transparency and reproducibility in enhanced molecular simulations (2019) — 1,046 citations
- Demonstrating the Transferability and the Descriptive Power of Sketch-Map. (2013) — 112 citations
- i-PI 2.0: A universal force engine for advanced molecular simulations (2018) — 334 citations
- A Data-Driven Construction of the Periodic Table of the Elements (2018) — 50 citations
- Nuclear quantum effects enter the mainstream (2018) — 406 citations
- i-PI: A Python interface for ab initio path integral molecular dynamics simulations (2014) — 260 citations
- From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map. (2011) — 283 citations
- Efficient stochastic thermostatting of path integral molecular dynamics. (2010) — 367 citations
- Colored-Noise Thermostats à la Carte (2010) — 261 citations