A Self-consistent Artificial Neural Network Inter-atomic Potential for Li/C Systems

Graphene-based structures, due to their large surface area, have been suggested as suitable anode materials for Li-ion batteries. In a previous study[1] we examined Li adsorption on graphene at finite temperature with a site-based potential and identified the temperature and Li coverage at which a transition can be expected from disperse Li ion to clustered Li atoms configuration on graphene surface. To extend this study to a wide range of Li coverage on realistic anode materials, a more flexible and accurate Li-C potential is needed. In this talk we first present a self-consistent approach to construct a neural network potential for Carbon using the PANNA code[2]. Our potential performs excellently in ranking the energies of distinct sp3 networks and reproduces the equation of state of graphite, diamond and graphene, as well as elastic and vibrational properties of these phases. We then extend our potential to Li/C systems incorporating long range electrostatics and test its performance on a wide range of Li adsorbed carbon allotropes.
[1] Y. Shaidu, E. Kucukbenli and S. de Gironcoli, J. Phys. Chem. C 122, 20800 (2018).
[2] R Lot, F Pellegrini, Y Shaidu, E Kucukbenli "PANNA: Properties from Artificial Neural Network Architectures", arXiv:1907.03055 , (2019).