activity
20222025
most citedA machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data

20 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2025

Direct observation of nanoscale pinning centers in Ce(Co0.8Cu0.2)5.4 permanent magnets

Nikita Polin, Shangbin Shen, Fernando Maccari +15

Permanent magnets containing rare earth elements are essential components for the electrification of society. Ce(Co1-xCux)5 permanent magnets are a model system known for their sub…

cond-mat.mtrl-sci2025

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…

cond-mat.mtrl-sci2024

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…

cond-mat.mtrl-sci2023★ 1 cited

On the role of Grain Boundary Character in the Stress Corrosion Cracking of Nanoporous Gold Thin Films

Aparna Saksena, Ayman El-Zoka, Alaukik Saxena +3

For its potential as a catalyst, nanoporous gold (NPG) prepared through dealloying of bulk Ag-Au alloys has been extensively investigated. NPG thin films can offer ease of handling…

physics.chem-ph2023★ 20 cited

A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data

Alaukik Saxena, Nikita Polin, Navyanth Kusampudi +7

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the q…

cond-mat.mtrl-sci2022★ 1 cited

On Strong-Scaling and Open-Source Tools for High-Throughput Quantification of Material Point Cloud Data: Composition Gradients, Microstructural Object Reconstruction, and Spatial Correlations

Markus Kühbach, Vitor Vieira Rielli, Sophie Primig +8

Characterizing microstructure-material-property relations calls for software tools which extract point-cloud- and continuum-scale-based representations of microstructural objects.…