Jonny Proppe

Materials Chemistry · 2021

A researcher in the Science corpus: 1 paper; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 55 citations into it
• Computational Theory and Mathematics — 31 citations into it
• Atomic and Molecular Physics, and Optics — 24 citations into it
• Molecular Biology — 17 citations into it
• Computational Chemistry & Drug Discovery — 16 citations into it
• Physical and Theoretical Chemistry — 13 citations into it
• AI for Physical Sciences — 8 citations into it

Whom Jonny Proppe trusts overall

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

1. J. Behler
2. A. P. Bart'ok
3. Matthias Rupp 
4. Stefan Grimme
5. Gábor Csányi
6. Stefan Chmiela
7. Michael Gastegger
8. Kristof T. Schütt
9. Volker L. Deringer
10. Michael W. Mahoney

Papers from Jonny Proppe's perspective

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

1. Generalized neural-network representation of high-dimensional potential-energy surfaces.
2. Machine learning unifies the modeling of materials and molecules
3. Machine learning of accurate energy-conserving molecular force fields
4. Machine learning based interatomic potential for amorphous carbon
5. Convolutional Networks on Graphs for Learning Molecular Fingerprints
6. Comparing molecules and solids across structural and alchemical space.
7. Accuracy and transferability of Gaussian approximation potential models for tungsten
8. Fast and accurate modeling of molecular atomization energies with machine learning.
9. Quantum-chemical insights from deep tensor neural networks
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

• Machine-learned potentials for next-generation matter simulations (2021) — 488 citations