A materials genomic approach for the discovery of electroactive molecules for grid-scale energy storage

Dr. Süleyman Er
Harvard University, Aspuru-Guzik Research Group, Dept of Chem and Chem Biology
Gorlaeus lab 06
11 Jul 2014 - 13:15
Dr. Süleyman Er_jpg.jpg

Flow batteries are a type of rechargeable batteries that offer promising new technologies for the grid-scale electrical energy storage. The design of flow batteries allows to store the reactants and the products outside the electrochemical conversion device. Thus, the device itself may be optimized for the required power, while the required energy is independently determined by the mass of reactant and the size of storage tanks. This provides possibilities in bringing down the storage cost per kWh, which is the single most challenging requirement for grid-scale electrical energy storage1. Large-scale utilization of flow batteries is limited due to the cost of redox-active and electrocatalytic precious metals2.

In a recent publication3, we showed that the incorporation of earth-abundant small organic molecules, such as quinones4, into aqueous flow batteries offers various advantages over the conventional flow battery technologies. These advantages include scalability, stability, kinetics, solubility, and tunability. Provided the opportunity to use quinones in flow batteries, the new objective is to find the best performing molecules that would meet the required properties and functionalities of a practical flow battery. The discovery of new quinone molecules is a formidable challenge due to the large chemical search space, and the traditional experimental and theoretical approaches are limited to the study of only a small set of molecules annually.

At Harvard, we develop high-throughput computational screening algorithms to quickly and accurately predict the materials properties. In this presentation, I will introduce our materials genomic approach on finding the new quinone molecules for aqueous flow batteries5. This approach is based on a digital design framework that operates by reducing the huge search space needed for materials, guiding processes, and integration of data from experiment, theory, and simulation6. I will present our latest results on: (1) performing first-principles high-throughput calculations for massive data generation, (2) developing quick and robust theoretical methods, (3) predicting quantitative structure property relationships, and (4) validating our theoretical models and computational results through experiments.

1.     J. Rugolo, and M. J. Aziz. Energy Environ. Sci., 5, 7151-7160 (2012).
2.     R. F. Service. Science, 344, 352-354 (2014).
3.     B. Huskinson, M. P. Marshak, C. Suh, S. Er, M. R. Gerhardt, C. J. Galvin, X. Chen, A. Aspuru-Guzik, R. G. Gordon, and M. J. Aziz. Nature, 505, 195–198 (2014).
4.     Quinone molecules: http://en.wikipedia.org/wiki/Quinone
5.     S. Er, C. Suh, M. P. Marshak, and A. Aspuru-Guzik. Submitted (2014).
6.     S. Curtaralo, G. L. W. Hart, M. B. Nardelli, N. Mingo, S. Sanvito, and O. Levy. Nature Materials, 12, 191-201 (2013).


Host: Prof. dr. Geert-Jan Kroes, g.j.kroes@chem.leidenuniv.nl tel 4396