| Project Detail |
Inverse design approach to creating high-performance materials for solar energy applications
Progress in sustainable energy technologies largely relies on the discovery of new earth-abundant materials with an unprecedented ability to conduct ions, catalyse reactions and transport photogenerated carriers. The EU-funded IDOL project will reverse the traditional material design process to discover and create materials that possess excellent optoelectronic properties and band gaps exceeding 1.5 eV. Initially, theoretical and experimental work will focus on phosphosulfides and will later extend to other material types. Phosphosulfides with the desired properties will be integrated into actual photovoltaic devices.
Progress in sustainable energy technology relies on the discovery of new earth-abundant materials with unprecedented ability to conduct ions, catalyze reactions, transport photogenerated carriers, etc. The main scientific question is how to find the materials with exactly the desired functionality from the huge pool of all possible materials (more than 10^12).
In IDOL, we will attempt to answer the long-standing question of inverse materials design. Our targeted functionality is high optoelectronic quality (i.e. long photocarrier lifetimes, high mobilities, and high absorption coefficient) in an earth-abundant semiconductor with band gap above 1.5 eV. This will be a breakthrough in three areas key to a sustainable energy future: multijunction photovoltaics, light-emitting diodes, and solar fuels.
The IDOL approach is a combination of experimental and computational research, focusing on the most device-relevant material form: thin films. Initially, we will restrict our search to the intriguing and still highly underexplored family of phosphosulfides (PSs). Later, we will extend our insights to other chemistries. From my preliminary investigation, many PSs should exhibit high mobilities and appropriate band gaps.
We will break the inverse design problem into logically connected steps: from application-specific figures of merit, going back to defect properties, generic optoelectronic properties, structure, growth conditions, and composition. We will exploit a unique combinatorial deposition system to grow candidate materials and characterize them using high throughput facilities at our host. For properties not experimentally accessible, we will employ first-principles calculations. This hybrid dataset will be analyzed step-by-step by human intelligence and machine learning to formulate design criteria and generate new materials with the desired properties. The discovered PS with the highest figures of merit will be incorporated into an actual photovoltaic device. |
