Computational Materials Design – Using atomic scale modelling to develop and optimise materials for next-generation energy technologies

Abstract

The need for high performing energy materials is ever increasing. Materials design plays a pivotal role in this. The development and optimization of high-performance anode materials for alternative and complementary battery technologies to the traditional lithium-ion batteries is a crucial challenge for the sustainable energy revolution.1–4 Atomic scale computational modelling using density functional theory (DFT) and molecular dynamics (MD) allows for efficient and targeted materials design from the nanoscale up, and is increasingly used by the scientific community as an integral part of material development.1,5,6 Computational modelling does not only provide fundamental insight, but also direct input for experimental synthesis, often where quantities cannot readily be obtained by other means. In this tutorial talk, I will give an overview of how different atomic scale modelling techniques can be used to probe challenges in energy materials including fuel cells, thermoelectrics, photovoltaics and batteries. A direct example of a computational materials design workflow will then be demonstrated by the work we conducted on carbon materials for sodium and potassium batteries were we combined computational and experimental methods.7–9

References

1. Olsson, E., Yu, J., Zhang, H., Cheng, H. & Cai, Q. Atomic‐Scale Design of Anode Materials for Alkali Metal (Li/Na/K)‐Ion Batteries: Progress and Perspectives. Adv. Energy Mater. 12, 2200662 (2022).

2. Morgan, L. M. et al. Pushing the boundaries of lithium battery research with atomistic modelling on different scales. Prog. Energy 4, 012002 (2022).

3. Huang, Y. T., Kavanagh, S. R., Scanlon, D. O., Walsh, A. & Hoye, R. L. Z. Perovskite-inspired materials for photovoltaics and beyond-from design to devices. Nanotechnology 32, 60 (2021).

4. Au, H. et al. A revised mechanistic model for sodium insertion in hard carbons. Energy Environ. Sci. 13, 3469–3479 (2020).

5. Urban, A., Seo, D. H. & Ceder, G. Computational understanding of Li-ion batteries. npj Comput. Mater. 2, 16002 (2016).

6. Savioli, J. & Watson, G. W. Computational modelling of solid oxide fuel cells. (2020) doi:10.1016/j.coelec.2019.12.007.

7. Olsson, E., Chai, G., Dove, M. & Cai, Q. Adsorption and migration of alkali metals (Li, Na, and K) on pristine and defective graphene surfaces. Nanoscale 11, 5274–5284 (2019).

8. Olsson, E. et al. Elucidating the Effect of Planar Graphitic Layers and Cylindrical Pores on the Storage and Diffusion of Li, Na, and K in Carbon Materials. Adv. Funct. Mater. 30, 1908209 (2020).

9. Olsson, E., Cottom, J. & Cai, Q. Defects in Hard Carbon: Where Are They Located and How Does the Location Affect Alkaline Metal Storage? Small 17, 2007652 (2021).