Multiscale Investigation of the Diffusion Mechanism within the Solid-Electrolyte Interface Layer: Coupling Quantum Mechanics, Molecular Dynamics, and Macroscale Mathematical Modeling

A dynamically equivalent atomistic electrochemical paradigm for the larger-scale experiments

Electrochemical systems possess a considerable part of modern technologies, such as the operation of rechargeable batteries and the fabrication of electronic components, which are explored both experimentally and computationally. The largest gap between the experimental observations and atomic-level simulations is their orders-of-magnitude scale difference. While the largest computationally affordable scale of the atomic-level computations is ∼ns and ∼nm, the smallest reachable scale in the typical experiments, using very high-precision devices, is ∼s and ∼μm. In order to close this gap and correlate the studies in the two scales, we establish an equivalent simulation setup for the given general experiment, which excludes the microstructure effects (i.e., solid-electrolyte interface), using the coarse-grained framework. The developed equivalent paradigm constitutes the adjusted values for the equivalent length scale (i.e., lEQ), diffusivity (i.e., DEQ), and voltage (i.e., VEQ). The time scale for the formation and relaxation of the concentration gradients in the vicinity of the electrode matches for both smaller scale (i.e., atomistic) equivalent simulations and the larger scale (i.e., continuum) experiments and could be utilized for exploring the cluster-level inter-ionic events that occur during the extended time periods. The developed model could offer insights for forecasting experiment dynamics and estimating the transition period to the steady-state regime of operation.

Computational Framework Combining Quantum Mechanics, Molecular Dynamics, and Deep Neural Networks to Evaluate the Intrinsic Properties of Materials

The design and evaluation of future nanomaterials with specific properties is a challenging task as the current traditional methods rely on trial and error approaches that are time-consuming and expensive. On the computational front, design tools such as molecular dynamics (MD) simulations help us reduce the costs and times. However, nonbonded potential parameters, the key input parameters for an MD simulation, are usually not available for designing and studying new materials. Resolving this, quantum mechanics (QM) calculations could be used to evaluate the system's energy as a function of the nonbonded distances, and the resulting data set could be fit to a generic potential equation to obtain the fitting constants (potential parameters). However, fitting this massive data set containing thousands of unknown parameters using traditional mathematical formulations is not feasible. Hence, most computational frameworks in the literature utilize several simplifications, leading to a severe loss of accuracy. Addressing this deficiency, in this work, we propose a multi-scale framework that couples QM calculations and MD with advanced deep neural networks to determine the potential parameters. This advanced framework has been extensively validated by employing it to predict properties such as the density, boiling point, and melting point of five different types of molecules that are well-understood, namely, the polar molecule H2O, ionic compound LiPF6, ethanol (C2H5OH), long-chain molecule C8H18, and the complex molecular system ethylene carbonate (EC).