Publications

Accelerating the adoption of research data management strategies
Medina, J., Ziaullah, A. W., Park, H., Castelli, I. E., Shaon, A., Bensmail, H., & El-Mellouhi, F. (2022). Matter, 5(11), 3614-3642.
Insights on the effect of water content in carburizing gas mixtures on the metal dusting corrosion of iron
Bentria, El Tayeb, et al. Applied Surface Science 579 (2022): 152138.
Democratizing the Assessment of Thermal Robustness of Metal–Organic Frameworks
Satyanarayana Bonakala, Anas Abutaha, Palani Elumalai, Ayman Samara, Said Mansour, and Fedwa El-Mellouhi ACS Omega 2022 7 (50), 46515-46523 DOI: 10.1021/acsomega.2c05345
Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials
Ziaullah, A.W., Chawla, S. & El-Mellouhi, F. Integr Mater Manuf Innov 12, 157–170 (2023).
Automated Model Training (AMT) GUI: An Opportunity for integrating AI in the Laboratory Experiment
Shakeel, M. B., Belhaouari, S. B., & Mellouhi, F. E. (2023). ArXiv. /abs/2311.13808
Efficient and Selective Electrochemical CO2 to Formic Acid Conversion: A First-Principles Study of Single-Atom and Dual-Atom Catalysts on Tin Disulfide Monolayers
Research on electrochemical CO2 conversion using single and dual-atom catalysts.
A human-in-the-loop approach for visual clustering of overlapping materials science data
Satyanarayana Bonakala, Michael Aupetit, Halima Bensmail and Fedwa El-Mellouhi Digital Discovery, 2024, 3, 502-513
Data-driven enhancement of cubic phase stability in mixed-cation perovskites
Park, Heesoo, et al. Machine Learning: Science and Technology 2.2 (2021): 025030.

FAIR Data Management

Advancing Materials Research with FAIR Principles
At AISCIA, we are committed to the FAIR principles (Findable, Accessible, Interoperable, and Reusable) in managing research data. Our approach ensures that materials science data is not only collected systematically but also shared in a manner that accelerates innovation and collaboration across the scientific community.

Our FAIR Data Initiatives:
  • Findable: We employ rich metadata and persistent identifiers to ensure all research data can be easily discovered by researchers worldwide.
  • Accessible: Our data repositories use standardized protocols, making data retrieval straightforward and efficient.
  • Interoperable: We utilize common data formats and vocabularies, enabling seamless integration with other datasets and tools.
  • Reusable: Clear usage licenses and detailed provenance information allow data to be repurposed for new discoveries.

By adhering to FAIR principles, we enhance the reproducibility and impact of our research, contributing to a more transparent and collaborative scientific ecosystem.