383 citations · 439 across the 9 of their papers we have counts for
9 papers
Machine learning enhanced atom probe tomography analysis: a snapshot review
Yue Li, Ye Wei, Alaukik Saxena +3
Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its signific…
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…
A mechanically strong and ductile soft magnet with extremely low coercivity
Liuliu Han, Fernando Maccari, Isnaldi R. Souza Filho +6
Soft magnetic materials (SMMs) serve in electrical applications and sustainable energy supply, allowing magnetic flux variation in response to changes in applied magnetic field, at…
Machine learning-enabled high-entropy alloy discovery
Ziyuan Rao, PoYen Tung, Ruiwen Xie +14
High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those wit…
Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data
Xuyang Zhou, Ye Wei, Markus Kühbach +6
Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great det…
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…
Machine-learning-enhanced time-of-flight mass spectrometry analysis
Ye Wei, Rama Srinivas Varanasi, Torsten Schwarz +8
Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a…