In a study published in Nature Chemical Engineering, researchers used an adaptive AI-guided robotic framework to reveal how molecular packing governs mixed ionic-electronic transport in electronic polymers.
Figure. AI/AE platform for accelerated exploration of mixed ion-electron conducting polymers.
Scientific Achievement
An adaptive AI-guided robotic framework helped reveal how molecular packing governs mixed ionic-electronic transport in electronic polymers, uncovering a previously unknown polymorph and key nanostructural features under data-limited conditions.
Significance and Impact
This work developed an AI-powered autonomous experimentation (AI/AE) platform with an AI advisor that enables adaptive decision-making and mitigates cognitive bias and data scarcity in autonomous, device-level electronic materials discovery.
Research Details
- Autonomous device fabrication and electrical characterization
- Adaptive AI guidance enabling efficient exploration of a large processing space
- Data-driven downselection linking morphology, polymorphism, and transport