output
20192025
most citedDScribe: Library of Descriptors for Machine Learning in Materials Science

857 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

6 papers

physics.comp-ph2025

Mesoscopic Modeling of Structure-Transport Relationships in Dense CNT Films Containing Amorphous Carbon

Yvelin Giret, Filippo Federici Canova, Al-Moatasem El-Sayed +6

Carbon nanotube (CNT) films are widely considered as prospective building blocks for advanced electronic and nanostructured materials. In particular, electrical transport in high-d…

cond-mat.mtrl-sci2025

Improving atomic force microscopy structure discovery via style-translation

Jie Huang, Niko Oinonen, Fabio Priante +4

Atomic force microscopy (AFM) is a key tool for characterising nanoscale structures, with functionalised tips now offering detailed images of the atomic structure. In parallel, AFM…

cond-mat.mtrl-sci2023★ 2 cited

First principles study on the segregation of metallic solutes and non-metallic impurities in Cu grain boundary

Vasileios Fotopoulos, Jack Strand, Manuel Petersmann +1

Metallic dopants have the potential to increase the mechanical strength of polycrystalline metals. These elements are expected to aggregate in regions of lower coordination, such a…

cond-mat.mes-hall2021★ 1 cited

Electrostatic Discovery Atomic Force Microscopy

Niko Oinonen, Chen Xu, Benjamin Alldritt +7

While offering unprecedented resolution of atomic and electronic structure, Scanning Probe Microscopy techniques have found greater challenges in providing reliable electrostatic c…

physics.comp-ph2019★ 121 cited

Automated Structure Discovery in Atomic Force Microscopy

Benjamin Alldritt, Prokop Hapala, Niko Oinonena +7

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces.…

cond-mat.mtrl-sci2019★ 857 cited

DScribe: Library of Descriptors for Machine Learning in Materials Science

Lauri Himanen, Marc O. J. Jäger, Eiaki V. Morooka +5

DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the applic…