Chemical bonds in crystals: a machine learning view
Master of Science in Engineering Physics
2021
Prof. Dr. Stefaan Cottenier
Predicting Phase Stability in Nitrogen Steels with Density-Functional Theory
Tue, 17/12/2019
Chemical bonds in crystals: a machine learning view
Master of Science in Engineering Physics
2020
Boosting the discovery rate of energy materials using deep learning
Master of Science in Engineering Physics
2020
Queue Manager: high-throughput density-functional theory screening software for materials discovery and design
Fri, 02/09/2016
Campus Sterre S9 Krijgslaan 281, 9000 Gent
Supervisors
Prof. Dr. Stefaan Cottenier, Prof. Dr. ir. Z. HensBoosting crystal discovery in quaternary space: from brute force to intelligent screening
Density Functional Theory as a tool to get more out of experimental data: case-studies for Al-Zn-O and for the interaction between Po and Pb-Bi-eutectic
Fri, 29/08/2014
Faculty of Engineering and Architecture, Jozef Plateaustraat, Ghent
Development of accurate force fields for a Pareto screening of high-performance metal-organic frameworks
Master of Science in Engineering Physics
2015
Predicting the Pb-Bi-Po and Pb-Bi-Te phase diagrams from first principles
Boosting the discovery rate of quaternary Zintl phases
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