Christoph Ortner

Materials Chemistry · 2009–2022

A researcher in the Science corpus: 5 papers; trusts 21 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 46 citations into it
• Computational Theory and Mathematics — 40 citations into it; #11,510 of 12,182 by trust here, #10,349 by citations
• Mechanics of Materials — 22 citations into it; #1,608 of 6,291 by trust here, #2,978 by citations
• Molecular Biology — 19 citations into it
• Computational Chemistry & Drug Discovery — 16 citations into it
• Atomic and Molecular Physics, and Optics — 15 citations into it
• Computational Mechanics — 15 citations into it; #4,506 of 10,001 by trust here, #6,041 by citations
• Physical and Theoretical Chemistry — 14 citations into it

Whom Christoph Ortner trusts overall

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

1. Susanne C. Brenner
2. Blaise Bourdin
3. Andrea Braides
4. Alessandro Giacomini
5. Ohannes A. Karakashian
6. Andris Lasis
7. Johannes Klicpera
8. G. Lancioni
9. Kristof T. Schütt
10. Gilles A. Francfort

Who trusts Christoph Ortner overall

1. Thomas Wick
2. Michael J. Borden
3. Mary F. Wheeler
4. Timo Heister
5. Sanghyu Lee
6. Thomas J. R. Hughes

Papers from Christoph Ortner's perspective

Ordered by how strongly the researchers Christoph Ortner 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. Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
3. A Density Result in Two-Dimensional Linearized Elasticity, and Applications
4. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
5. Directional Message Passing for Molecular Graphs (International Conference on Learning Representations)
6. Cormorant: Covariant Molecular Neural Networks
7. Numerical implementation of the variational formulation for quasi-static brittle fracture
8. Generalized neural-network representation of high-dimensional potential-energy surfaces.
9. Machine learning of accurate energy-conserving molecular force fields
10. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.

Their papers

• MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields (2022) — 1,193 citations
• Compact embeddings of broken Sobolev spaces and applications (2009) — 145 citations
• The design space of E(3)-equivariant atom-centred interatomic potentials (2022) — 361 citations
• An adaptive finite element approximation of a generalized Ambrosio-Tortorelli functional (2013) — 86 citations
• EXISTENCE OF SOLUTIONS TO A REGULARIZED MODEL OF DYNAMIC FRACTURE (2010) — 143 citations