6 papers
Dual-Resonator Organic Microcavities for Triplet-Triplet Annihilation Upconversion
Robert H. Gordon, Rachel C. Kilbride, Michael Hutton +2
Solid-state triplet-triplet annihilation upconversion offers a compelling route to harvest sub-bandgap photons to increase the efficiency of photovoltaics, but low sensitizer dopin…
Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
Xianyuan Liu, Charles Anjah, Benjamin E. Jolly +9
Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel…
Understanding surface potential dynamics of passivated perovskites via Kelvin Probe Force Microscopy
Rehmat Sood-Goodwin, Xue-Li Cao, Benjamin C. Kinvig +4
Molecular passivation has become central to reducing photovoltage losses in metal-halide perovskite solar cells, but its electronic action is still often inferred from device-level…
Disentangling the origin of degradation in perovskite solar cells via optical imaging and Bayesian inference
Akash Dasgupta, Robert D. J. Oliver, Manuel Kober-Czerny +5
Machine learning and computational inference, coupled with experimental data, promise to significantly accelerate our rate of learning in most scientific disciplines. In this study…
Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings
Haolin Wang, Xianyuan Liu, Anna Jungbluth +3
Accurate bandgap prediction is crucial for semiconductor applications, yet machine learning models trained on computational data often struggle to generalize to experimental bandga…
Strong coupling in molecular systems: a simple predictor employing routine optical measurements
Marie S. Rider, Edwin C. Johnson, Demetris Bates +6
We provide a simple method that enables readily acquired experimental data to be used to predict whether or not a candidate molecular material may exhibit strong coupling. Specific…