37 citations · 43 across the 5 of their papers we have counts for
5 papers
3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures
Jiwei Yu, Zhangwei Wang, Aparna Saksena +8
Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a piv…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…
Machine learning-enabled tomographic imaging of chemical short-range atomic ordering
Yue Li, Timoteo Colnaghi, Yilun Gong +13
In solids, chemical short-range order (CSRO) refers to the self-organisation of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a…
Reflections on the spatial performance of atom probe tomography in the analysis of atomic neighbourhoods
Baptiste Gault, Benjamin Klaes, Felipe F. Morgado +5
Atom probe tomography is often introduced as providing "atomic-scale" mapping of the composition of materials and as such is often exploited to analyse atomic neighbourhoods within…
Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys
Yue Li, Xuyang Zhou, Timoteo Colnaghi +6
Nanoscale L12-type ordered structures are widely used in face-centred cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-s…