37 citations · 52 across the 4 of their papers we have counts for
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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…