Data driven and physics inspired models for hopping transport in organic field effect transistors

December 2021: Scientific Reports

https://www.nature.com/articles/s41598-021-02737-7

Despite the surfeit in the amount of research interest towards organic semiconductors in the last few decades, the underlying charge transport picture still remains hazy with no consensus among the different models. Here, we employ a purely data-driven approach, namely symbolic regression, on temperature-dependent field-effect mobility data of different organic semiconductors to describe the transport and compare the same with a physics-inspired renormalization fitting approach where we used a scaled, dimensionless mobility with respect to a scale-invariant reference temperature. We find that the renormalization approach is powerful compared to purely data-driven symbolic regression, providing an intuitive understanding of data with extrapolative ability.

Extrapolative Bayesian Optimization with Gaussian Process surrogate models

September 2021: Advanced Intelligent Systems

https://onlinelibrary.wiley.com/doi/10.1002/aisy.202100101

Bayesian Optimization (BO) has emerged as the algorithm of choice for driving autonomous high throughput experiments. However, some recent theoretical and experimental studies cast doubt on the efficiency of the default Gaussian Process (GP) surrogate model, especially for optimization of complex datasets. Here, we performed a systematic optimization study using simulated experiments, and compared various surrogate models for BO including GP and also Neural Network Ensemble. We found that with proper choice of the GP kernel and parameters, the GP surrogate model is still the most efficient, allowing the BO to extrapolate fastest towards the optimum after 100 experimental iterations.

The perspective on photocatalytic overall water splitting using organic materials

July 2021: Journal of Materials Chemistry A

https://pubs.rsc.org/en/content/articlehtml/2021/ta/d1ta03710b

Solar water splitting offers a potential avenue for the production of clean and storable energy in the form of hydrogen. Semiconductors can be used as photocatalysts that enable the simultaneous production of hydrogen and oxygen from water via water splitting and in recent years inorganic semiconductor photocatalysts have been significantly improved in terms of their performance with organic semiconductors emerging as a potential alternative, though mostly studied for sacrificial half-reactions. Herein, Yang and co-authors present recent developments in using organic semiconductors as photocatalysts highlighting their potential due to their synthetic tunability. We particularly focus on their application in overall water splitting without using sacrificial reagents and suggest future directions in using these materials in large scale application before concluding with suggestions for the wider community to focus research efforts on particular challenges in the field and opportunities that organic materials offer.