Structure and Dielectric Properties of Aqueous LiOH Solutions Using Neural Network Quantum Molecular Dynamics

The excellent solvation properties of water are responsible for its role in life-sustaining biological processes and for its importance to several technological applications. The structure and dynamics of aqueous solutions is highly complex, composed of transient hydrogen bonding and continuously reorganized solvation shells, which are difficult to characterize experimentally. Further, the dynamical response of these systems that are dominated by the restructuring of the hydrogen bond network are still unknown. In this study, we use neural network quantum molecular dynamics (NNQMD) to capture quantum accurate molecular configurations and evolution of the hydrogen bond network in an aqueous solution of LiOH as a function of concentration. We further probe the dynamic response of LiOH solutions by quantifying the dielectric constant, \varepsilon_0 using the variance of the dipole moment along a long molecular dynamics trajectory in the canonical ensemble. The polarization fluctuations are computed with quantum accuracy using a secondary neural network that uses Wannier Functions to encode many-body polarization effects.<br/><u>Acknowledgments</u>: This work was supported as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award Number DE-SC0014607. The simulations were performed at the Argonne Leadership Computing Facility under the DOE INCITE and Aurora Early Science programs and at the Centre for Advanced Research and Computing of the University of Southern California.