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An Automated Workflow to Discover the Structure-Stability Relations for Radiation Hard Molecular Semiconductors
Andreas J Bornschlegl1, Patrick Duchstein2, Jianchang Wu1,3
1Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 7, 91058 Erlangen, Germany.
Researchers developed a machine learning approach to predict radiation-hard molecular semiconductors for space applications. This method accelerates the discovery of stable organic materials by analyzing degradation under ultraviolet-C light, identifying key structural features for enhanced resilience.
Area of Science:
- Materials Science
- Organic Electronics
- Photovoltaics
Background:
- Radiation-hard molecular semiconductors are crucial for space applications like photovoltaics.
- Current methods lack predictive power for material resilience against extraterrestrial radiation.
Purpose of the Study:
- To accelerate the discovery of radiation-hard molecular semiconductors.
- To establish design principles for predicting material stability against ultraviolet-C (UVC) radiation.
Main Methods:
- High-throughput screening, lab automation, and machine learning were combined.
- A library of over 130 organic hole transport materials was processed, degraded under UVC, and measured.
- Gaussian Process Regression was used with structural fingerprints to identify structure-stability relationships.
Main Results:
- A stability ranking spanning three orders of magnitude was achieved.
- Fused aromatic ring clusters were found to be beneficial for UVC stability.
- Thiophene, methoxy, and vinylene groups were identified as detrimental to stability.
Conclusions:
- A predictive model was established to quantify the impact of molecular features on UVC stability.
- The findings enable chemists to incorporate UVC stability into molecular design strategies.
- Future work aims to inversely design high-performance, radiation-hard molecular semiconductors.
