Industry Applications

Data Digitalization Strategies for AI-guided Experimentation

Project Lead: Mohammed Zniber

AI is transforming material and process optimization, emphasizing the need for high-quality data in research. This project addresses the Finnish photonics sector’s needs by collaborating with industry and academia to enhance manufacturing through AI.

Partner(s): Tampere University, VTT, Reflekron, Comptek Solutions, Senop

Optimization of a Multilayer System for Anti-reflective Coatings

Project Lead: Mohammed Zniber

Anti-reflective coatings are crucial in applications such as eyeglasses, camera lenses, and solar panels, where enhancing light transmission is essential. By minimizing reflectance, these coatings improve visual clarity, image quality, and energy efficiency.

Aim: Utilize high-throughput search to identify stacks with minimal reflectance.

Partner(s): Senop

AI for Optimising MBE Thin Film Growth

Project Lead: Vadim Gorshanov

Molecular beam epitaxy facilitates the growth of high-quality crystal structures of functional materials with targeted properties for devices. We partner with an MBE instrument manufacturer to implement monitoring during the MBE process and AI-driven optimisation of resulting material quality and functionality.

Partner(s): DCA Instruments

AI-driven optimization of optical properties of zeolite materials

Project Lead: Tomasz Galica

Modern synthesis methods provide remarkable control over material design, however achieving materials with specific optical properties remains a significant challenge. A promising approach is reverse engineering, where desired properties dictate the material design. This project leverages machine learning and data science to establish a chemical-computational pipeline. This pipeline will recommend optimal synthesis properties for zeolite materials, to improve their optical properties.

Partner(s):Prof. Mika Lastusaari, Intelligent Materials Chemistry, Department of Chemistry, University of Turku