Anas & Pawan correlate charge and TE transport to paracrystallinity in conducting polymers

Jan 2020: Nature Communications https://www.nature.com/articles/s41467-020-15399-2

The conceptual understanding of charge transport in conducting polymers is still ambiguous due to a wide range of paracrystallinity (disorder). Here, we advance this understanding by presenting the relationship between transport, electronic density of states and scattering parameter in conducting polymers. We show that the tail of the density of states possesses a Gaussian form confirmed by two-dimensional tight-binding model supported by Density Functional Theory and Molecular Dynamics simulations. Furthermore, by using the Boltzmann Transport Equation, we find that transport can be understood by the scattering parameter and the effective density of states. Our model aligns well with the experimental transport properties of a variety of conducting polymers; the scattering parameter affects electrical conductivity, carrier mobility, and Seebeck coefficient, while the effective density of states only affects the electrical conductivity. We hope our results advance the fundamental understanding of charge transport in conducting polymers to further enhance their performance in electronic applications.

Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

August 2018 - Joule : Cell Press

https://www.sciencedirect.com/science/article/pii/S2542435118302289

In collaboration with Prof. Tonio Buonassisi (MIT), we lay out the pathway for increasing the speed of discovery.

Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by ten times or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return on investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-performance computing concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transformthe way we perform materials research, with considerable first-mover advantages at stake.

Wu Jing uses vacancy-induced Kondo hybridization to tune the thermoelectric transport in 2D MoS2

June 2020 - Proceedings of National Academy of Sciences

https://www.pnas.org/content/117/25/13929.short

In a careful and seminal study that took 3 years, a very careful study and analysis, we show that new physics such as Kondo scattering can enhance thermoelectric properties substantially!

The study of correlated phenomena in 2D semiconductors opens up new pathways toward understanding and engineering material functionalities (such as thermoelectrics) in easily accessible van der Waals solids. Local structural defects such as vacancies inevitably exist in natural as well as synthetic TMD crystals and have been predicted to serve as magnetic impurities capable of enhancing the strongly correlated effect. Herein we discover unusual thermoelectric behavior in sulfur vacancy-enriched MoS2 by rationally selecting h-BN as the substrate. We demonstrate that the thermoelectric transport properties can be strongly manipulated by vacancy-induced Kondo hybridization. A significant enhancement of thermoelectric power factor by two orders of magnitude is achieved in the MoS2/h-BN device.