Christoph Schran

Materials Chemistry · 2018–2023

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Christoph Schran trusts overall

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

  1. J. Behler

Papers from Christoph Schran's perspective

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

  1. Generalized neural-network representation of high-dimensional potential-energy surfaces.
  2. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
  3. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
  4. PyTorch: An Imperative Style, High-Performance Deep Learning Library
  5. Accurate and numerically efficient r$^2$SCAN meta-generalized gradient approximation
  6. Scaling properties of adsorption energies for hydrogen-containing molecules on transition-metal surfaces. (Physical Review Letters)
  7. Highly efficient separation of carbon dioxide by a metal-organic framework replete with open metal sites (Proceedings of the National Academy of Sciences of the United States of America)
  8. Square ice in graphene nanocapillaries (Nature)
  9. Computational and Experimental Studies on the Adsorption of CO, N2, and CO2 on Mg-MOF-74
  10. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Their papers