Alaukik Saxena

Alaukik Saxena is a Researcher at the Department of Computational Materials Design, Max Planck Institute for Sustainable Materials. Their work focuses on applying machine learning techniques to analyze atom probe tomography (APT) datasets for advanced material design. Saxena holds a B.Eng. in Mechanical Engineering from Panjab University (2016), an M.Sc. in Material Science and Simulation from Ruhr University Bochum (2020), and is a PhD candidate at the Max-Planck Institut für Eisenforschung GmbH (2020–2023).

Research interests include defect chemistry, microstructure analysis, and data-driven materials science. Saxena’s work bridges experimental techniques like APT and scanning transmission electron tomography (STEM) with machine learning to quantify chemical segregation, grain boundary roles in corrosion, and optimize permanent magnet performance. They also develop open-source tools for high-throughput material data analysis.

Publications emphasize APT data workflows, microstructural feature quantification, and roadmap strategies for data-centric materials science. Their contributions highlight theoretical advancements in nanoporous materials and θ′ precipitation in Al-Cu alloys. Saxena collaborates within the Defect Chemistry and Spectroscopy group, advancing computational phase studies and material defect engineering.

Awards and grants are not explicitly listed, but their research aligns with sustainable materials innovation. Advising and team leadership are inferred through collaborative projects in computational materials design and defect chemistry.