The primary objective of the current research program is to develop novel methods for computer-aided design of energy-relevant materials employing first-principles theory. More specifically, our research focuses on the organic materials for electric energy storage. The organic electrode materials (OEM), for sustainable battery application, are arising as a promising alternative, which bring together the following aspects: (i) they can be produced from abundant raw materials and (ii) they are highly versatile displaying tunable properties that can meet end-user-specific demands (1). However, there are drawbacks associated with their stability and low energy storage capacity. One important property that will also be the focus of this research is the recently discovered superlithiation states (2). We have successfully employed evolutionary algorithms interplayed with density functional theory (DFT) to resolve the crystal structures of compounds made of small organic molecules displaying carboxylic units at different lithiation stages (3). Such structures are very difficult to obtain experimentally requesting the development of sophisticated in operando spectroscopy techniques. Here, we will expand this study to develop a high-throughput computational materials design (HCMD) (4) approach to search for improved OEMs. The idea is simple: use supercomputers to virtually study hundreds or thousands of chemical compounds at a time, quickly and efficiently looking for the best building blocks for a new material. The computational time requested here will be used to develop a database needed for the HCMD, that has already been initiated in my group, and also to advance our fundamental understanding of the lithiation process in the organic compounds. The DFT calculations will be carried out using VASP (5) and QuantumATK (6) software and for the evolutionary algorithm we will use the USPEX software (7). My research group has already extensive experience using these codes and all of them are optimized to run in the SNIC platforms requested here. Furthermore, we will develop a machine learning based methodology using neural network (NN) to establish chemical structure–symmetry–electronic property relationships. Such work is already under development in my research group and we have already been able to obtain a NN-model to predict redox potentials of organic molecular systems by-passing the time consuming first-principles calculations.
(1) Häupler, B.; Wild, A.; Schubert, U.S. Adv. Energy Mater. 2015, 5 (11).
(2) Lee, H. H.; Park, Y.; Shin, K. H.; Lee, K. T.; Hong, S. Y. ACS Appl. Mater. Interfaces 2014, 6 (21), 19118–19126.
(3) C. Marchiori, D. Brandell, and C. Moyses Araujo J. Phys. Chem. C 2019, 123, 8, 4691-4700.
(4) Curtarolo, S. et al. Nature Materials 12, 191–201 (2013).