AIMATER is a cutting-edge platform that leverages the power of supercomputing and state-of-the-art methods. It provides open web-based access to a vast repository of computed information on both known and predicted materials. With AIMATER, researchers, scientists, and engineers can explore a wealth of data and utilize powerful analysis tools to inspire and design novel materials. The platform’s flagship app, CollaborationHub, offers a collaborative environment where experts from various disciplines can come together, exchange ideas, and drive innovation in materials science. Harnessing the potential of AIMATER and CollaborationHub, the possibilities for groundbreaking discoveries and advancements in material design are limitless.

Platforms

Perovskite Predictors

ML predicted energy landscape of a lead based and lead free perovskite.

Collaboration Hub

A data sharing and querying platfrom for catalysis surface reactions.

Automated Model Training (AMT)

A user-friendly interface designed to facilitate the seamless training of machine learning models.

Bentria, El Tayeb, et al. “Insights on the effect of water content in carburizing gas mixtures on the metal dusting corrosion of iron.” Applied Surface Science 579 (2022): 152138.
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
Ziaullah, A.W., Chawla, S. & El-Mellouhi, F. Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials. Integr Mater Manuf Innov 12, 157–170 (2023).
Shakeel, M. B., Belhaouari, S. B., & Mellouhi, F. E. (2023). Automated Model Training (AMT) GUI: An Opportunity for integrating AI in the Laboratory Experiment. ArXiv. /abs/2311.13808
Satyanarayana Bonakala, Michael Aupetit, Halima Bensmail and Fedwa El-Mellouhi Digital Discovery, 2024, 3, 502-513 10.1039/D3DD00179B

Park, Heesoo, et al. “Data-driven enhancement of cubic phase stability in mixed-cation perovskites.” Machine Learning: Science and Technology 2.2 (2021): 025030.

Team behind AIMATER

Dr. Fedwa El-Mellouhi

felmellouhi@hbku.edu.qa

Johanne Medina

jmedina@hbku.edu.qa

Abdul Wahab Ziaullah

awahab@hbku.edu.qa

Bilal Shakeel

mosh51726@hbku.edu.qa

Address

Qatar Environment Energy Research Institute

Phone

+974 44547284

Email Address

info@aiscia.com

Contact Us