Symposium EL14-High Throughput Discovery of the Next-Generation Semiconductors for Opto-Electronics

New types of materials utilized in opto-electronic devices can have a disruptive effect on the evolution of new technologies. In the recent past, this has been demonstrated by the emergence of halide perovskite semi-conductors in photovoltaics, which has revitalized photovoltaics research and expanded into related domains (photocatalysis, LEDs, scintillators, LASER, photo-catalysis).

Halide perovskite materials are a fascinating class of materials with a multitude of intriguing properties. Although remarkable power conversion efficiencies in photovoltaic devices have been demonstrated, knowledge gaps remain and include fundamental aspects related to formation kinetics and formation pathways. These significantly depend on the combination of synthetic variables and precursor chemistry.

In this respect combinatorial screening and high throughput synthesis present an excellent avenue to explore the composition space of emerging materials like metal halide perovskites further through high-throughput compositional screening methods. Furthermore, the methodology can also be systematically expanded to explore the composition-processing-property relationships of new material classes and aid in the optimization of processing conditions to obtain high-quality semi-conductors.

In this symposium we want to bring together the high-throughput materials discovery community focusing on the synthesis and analysis of emerging semiconductors for opto-electronic devices such as halide perovskites. The symposium will cover all aspects of the materials discovery cycle: high-throughput and combinatorial synthesis, high-throughput characterization, and accelerated as well as ML-assisted data analysis. A particular focus will be the utilization of robotized sample synthesis and analysis as well as data handling that can enable future autonomous materials discovery cycles.

Topics will include:

High throughput synthesis & characterization
Automation of synthesis and characterization
Data analysis: correlation of high throughput experiments and theoretical data
Scalable, sustainable and autonomous fabrication cycles
Key performance identifiers to evaluate materials technological potential in early development stages
Machine-learning guided materials discovery
Big Data Material Science and Open Data Platforms for Collaborative Research

Invited Speakers:

Mahshid Ahmadi (University of Tennessee, USA)
Hannah-Noa Barad (Max Planck Institute, Germany)
Christoph Brabec (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany)
Tonio Buonassisi (Massachusetts Institute of Technology, USA)
Emory Chan (Lawrence Berkeley National Laboratory, USA)
Claudia Draxl (Humboldt-Universität zu Berlin, Germany)
Alessio Gagliardi (Technische Universität München, Germany)
Marina Leite (University of California, Davis, USA)
Carolin Sutter-Fella (Lawrence Berkeley National Laboratory, USA)
Per Svensson (RISE Research Institutes of Sweden, Sweden)
Su-Huai Wei (Beijing Computational Science Research Center, China)
Jens Wenzel Andreasen (Technical University of Denmark, Denmark)