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).

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

The solid-electrolyte interface (SEI) layer has a critical role in Li-ion batteries' (LIBs) life span. The SEI layer, even in modern commercial LIBs, is responsible for more than 50% of capacity loss. Due to the inherent complexity in studying the SEI layer, many aspects of its performance and characteristics, including diffusion mechanisms in this layer, are unknown. As a result, most mathematical models use a constant value of the diffusion coefficient, instead of a variable formulation, to predict LIBs' properties and performance such as capacity fading and the SEI growth rate. In this work, by employing a multiscale investigation using a combination of quantum mechanics, molecular dynamics, and macroscale mathematical modeling, some equations are presented to evaluate the energy barrier against diffusion and the diffusion coefficient in different crystal structures in the inner section of the SEI layer. The equations are evaluated as a function of temperature and concentration and can be used to study the diffusion mechanism in the SEI layer. They can also be integrated with other mathematical models of LIBs to increase the accuracy of the latter.