activity
20192022
most citedMachine learning-enabled high-entropy alloy discovery

13 citations · 28 across the 4 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci202213 cited

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…

cond-mat.mtrl-sci202211 cited

Ab initio calculation of the magnetic Gibbs free energy of materials using magnetically constrained supercells

Eduardo Mendive-Tapia, Jörg Neugebauer, Tilmann Hickel

We present a first-principles approach for the computation of the magnetic Gibbs free energy of materials using magnetically constrained supercell calculations. Our approach is bas…

cond-mat.mtrl-sci2021

Understanding alkali contamination in colloidal nanomaterials to unlock grain boundary impurity engineering

Se-Ho Kim, Su-Hyun Yoo, Poulami Chakraborty +10

Metal nano-aerogels combine a large surface area, a high structural stability, and a high catalytic activity towards a variety of chemical reactions. The performance of such nanost…

cond-mat.mtrl-sci20214 cited

Ab initio investigations of point and complex defect structures in B2-FeAl

Halil İbrahim Sözen, Tilmann Hickel, Jörg Neugebauer

In this work we have studied the defect structure and corresponding defect concentration investigations through the theoretical, experimental and computational works on B2-type Fe-…

cond-mat.mtrl-sci2019

Imaging individual solute atoms at crystalline imperfections in metals

Shyam Katnagallu, Leigh T. Stephenson, Isabelle Mouton +11

Directly imaging all atoms constituting a material and, maybe more importantly, crystalline defects that dictate materials' properties, remains a formidable challenge. Here, we pro…