(186g) A Deep Learning Potential to Study Large-Scale Anhydrous Proton Transport Systems

Designing and studying new proton conducting materials are essential to operate proton exchange membranes fuel cells at intermediate temperatures and conditions of low humidity. We use graphane functionalized with hydroxyl groups (graphanol) as the material of interest. Previous methods to study the occurrences of proton conduction of graphanol involved performing highly expensive density functional theory (DFT) calculations¹. To overcome this computational barrier, we made use of dense neural networks. Our previous work with using deep-learning potentials (DP) for the precursor material of graphanol, graphane, showed tremendous capabilities of capturing molecular physics with small training data sets. We have developed a DP for graphanol that is capable of reproducing DFT forces to very high accuracies, within meV/Å accuracy. This DP is also able to simulate proton hopping, aided by the rotation of hydroxyl groups. We used the DeePMD² formalism along with an active learning scheme (DP-GEN)³ to develop a DP for graphanol. We show that our DP is capable of accurately predicting phonon, dynamic properties, and proton self-diffusion coefficients of graphanol for larger system sizes and varying operating temperatures. We also test this DP by varying the number of added protons to the graphanol system. Future work will involve testing this DP for larger multi-layered graphanol systems and to understand charge build up and polarization at interfaces of the multi-layered systems.

References

1. Bagusetty, A.; Johnson, J. K., Unraveling anhydrous proton conduction in hydroxygraphane. The journal of physical chemistry letters 2019, 10 (3), 518-523.
2. Wang, H.; Zhang, L.; Han, J.; Weinan, E., DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Computer Physics Communications 2018, 228, 178-184.
3. Zhang, Y.; Wang, H.; Chen, W.; Zeng, J.; Zhang, L.; Wang, H.; Weinan, E., DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models. Computer Physics Communications 2020, 253, 107206.