(360x) Studying Anhydrous Proton Transport on Graphene-Based Materials Using Deep Learning Methods
AIChE Annual Meeting
Tuesday, November 15, 2022 - 3:30pm to 5:00pm
Proton conductivity involves charge transfer and therefore cannot be fully described by observing motion of atoms alone. However, our graphanol DP potential does not include any information about electron density, charge polarization, or transport of charge centers. We have developed a deep learning formalism for predicting charge densities only using atomic positions for molecules and periodic systems. Our method, called DeepCDP (Deep-learning Charge Density Prediction), can predict electron densities for arbitrarily large systems. A DeepCDP model is trained on electron densities generated from DFT-MD simulations. Smooth Overlap of Atomic Orbitals (SOAP) is used to fingerprint atomic environments to their corresponding electron densities on a grid-point basis. Our DeepCDP model for graphanol achieves electron density prediction accuracies of >99% for both charged and uncharged systems. We demonstrate methods to use these predicted electron densities to compute the diffusion of the center of charge from DP simulations of graphanol with excess protons. Our combination of DeePMD and DeepCDP provides a complete description of proton conductivity solely using deep learning methods.