Identification number: LZP-2024/1-0406
Type: Latvian Council of Science Fundamental and Applied Research project
Duration: 01.01.2025. - 31.12.2027.
Project Leader: Dr. Sergejs Piskunovs, Institute of Solid State Physics University of Latvia (ISSP UL)
Total funding: 300 000 EUR
Project summary:
The proposed project aims to develop novel heterojunction photocatalysts for efficient green hydrogen production using a combination of generative machine learning and density functional theory (DFT) calculations. The research builds upon recent advancements in machine learning-integrated photocatalysis, intelligent design of energy catalytic materials, high-throughput modeling of 2D materials, and intelligent algorithms for photocatalyst design and performance prediction. The main objectives are to develop a DFT-supported generative machine learning framework for predicting the electronic properties and photocatalytic performance of novel heterojunction photocatalysts, experimentally validate the predicted photocatalysts and optimize their synthesis conditions, and to establish a database of promising photocatalysts and their properties for future research and development. The project will leverage the expertise of a multidisciplinary team, including researchers from the Institute of Solid State Physics, University of Latvia, and international collaborators. The expected outcomes include high-impact publications, conference presentations, and a publicly accessible database of photocatalysts. The proposed research has the potential to accelerate the discovery of efficient and cost-effective photocatalysts for green hydrogen production, contributing to the development of sustainable energy solutions and the transition towards a more environmentally friendly future.