Dr. Titan Hartono Machine Learning for Perovskite Solar Cells Research & Development

Guest talk

Dr. Titan Hartono, HZB,Department Novel Materials and interfaces for photovoltaic solar cells

Machine Learning for Perovskite Solar Cells Research & Development

Machine learning (ML) has been incorporated into R & D workflow to accelerate materials development, including perovskite solar cells (PSCs). This talk will give an overview of how ML can be incorporated into PSCs R & D, with the emphasis on a small, experimental dataset.

PSCs contain organic-inorganic perovskite absorber and its solution-based deposition method uses various organic solvents and anti-solvents. How do we optimize these materials? The first part of the talk will focus on the organic constituents screening of the PSCs and how we can derive insights from it.

In addition, to push for PSCs commercialization, PSCs’ short-lifetime issue needs to be tackled. This issue can be investigated using ML. The second part of the talk will focus on PSCs degradation data clustering, which gives an overview on the relationship between efficiency versus stability.

Short biography

Titan Hartono is currently a Postdoctoral Researcher in Solar Energy-Active Materials and Interfaces for Stable Perovskite Solar Cells Department at HZB, Germany, led by Prof. Antonio Abate. Her research focuses on accelerating perovskite solar cells (PSCs) exploration process, to find a stable, non-toxic, high-efficiency PSC compositions. Before coming to HZB, she did her PhD and her postdoctoral at MIT Photovoltaics Research Laboratory.