Aziz Ben Ali
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Aziz Ben Ali has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Alessio Gagliardi
Technical University of Munich Hans-Piloty-Straße 1
1 shared publication - David Gryc
TUM School of Natural Sciences Department of Chemistry Chair of Inorganic and Metal‐Organic Chemistry Catalysis Research Center Technical University of Munich Garching Germany
1 shared publication - E. Eja Petersen
Chair of Inorganic and Metal-Organic Chemistry, Technical University of Munich, Garching, Germany
1 shared publication - Ioannis Kouroudis
TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
1 shared publication - Joerg Ralf Jinschek
National Centre for Nano Fabrication and Characterization (DTU Nanolab, Technical University of Denmark, Fysikvej 307, Kongens Lyngby, Denmark, DK-2800
1 shared publication Maissa Mansour
TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
1 shared publication
Latest Publications
- Open Access
- 16 January 2026
- Published by American Chemical Society (ACS) in ChemRxiv
Data-Driven Transfer Learning Across MOF-Derived Zirconia Polymorphs
Despite extensive investigation of metal-organic framework (MOF) derived materials over the last 20 years, no systematic approach to predict the structural properties of the derived metal oxides is available. We present an integrated machine learning (ML) approach leveraging Smooth Overlap of Atomic Positions (SOAP) and multiple ML models, including Kernel Ridge Regression (KRR), to predict thermally derived zirconium dioxide (ZrO2) polymorph from diverse Zr-based precursors. By a systematic experimental dataset of calcination parameters, we train the ML model to quantitatively forecast material properties and the weight fraction of crystalline phases of the resulting ZrO2. Experimental validation of model predictions confirms that the chemical composition of precursors and calcination parameters have a profound influence on the crystallinity of MOF-derived ZrO2 polymorph. Our findings demonstrate the utility of small-data-driven predictive ML modeling and transfer learning for guiding the synthesis of advanced oxide materials providing a blueprint for accelerated discovery of MOF-derived nanomaterials.
- 16 January 2026
- Published by American Chemical Society (ACS) in ChemRxiv
Open AccessData-Driven Transfer Learning Across MOF-Derived Zirconia Polymorphs
Despite extensive investigation of metal-organic framework (MOF) derived materials over the last 20 years, no systematic approach to predict the structural properties of the derived metal oxides is available. We present an integrated machine learning (ML) approach leveraging Smooth Overlap of Atomic Positions (SOAP) and multiple ML models, including Kernel Ridge Regression (KRR), to predict thermally derived zirconium dioxide (ZrO2) polymorph from diverse Zr-based precursors. By a systematic experimental dataset of calcination parameters, we train the ML model to quantitatively forecast material properties and the weight fraction of crystalline phases of the resulting ZrO2. Experimental validation of model predictions confirms that the chemical composition of precursors and calcination parameters have a profound influence on the crystallinity of MOF-derived ZrO2 polymorph. Our findings demonstrate the utility of small-data-driven predictive ML modeling and transfer learning for guiding the synthesis of advanced oxide materials providing a blueprint for accelerated discovery of MOF-derived nanomaterials.