Ion Transport in Polymer Electrolytes: Building New Bridges between Experiment and Molecular Simulation

PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems

Electrochemical energy storage and conversion play increasingly important roles in electrification and sustainable development across the globe. A key challenge therein is to understand, control, and design electrochemical energy materials with atomistic precision. This requires inputs from molecular modeling powered by machine learning (ML) techniques. In this work, we have upgraded our pairwise interaction neural network Python package PiNN via introducing equivariant features to the PiNet2 architecture for fitting potential energy surfaces along with PiNet2-dipole for dipole and charge predictions as well as PiNet2-χ for generating atom-condensed charge response kernels. By benchmarking publicly accessible data sets of small molecules, crystalline materials, and liquid electrolytes, we found that the equivariant PiNet2 shows significant improvements over the original PiNet architecture and provides a state-of-the-art overall performance. Furthermore, leveraging on plug-ins such as PiNNAcLe for an adaptive learn-on-the-fly workflow in generating ML potentials and PiNNwall for modeling heterogeneous electrodes under external bias, we expect PiNN to serve as a versatile and high-performing ML-accelerated platform for molecular modeling of electrochemical systems.

Composite Polymer Solid Electrolytes for All-Solid-State Sodium Batteries

Sodium-ion batteries (SIBs) are emerging as a promising alternative to lithium-ion batteries, primarily due to their plentiful raw materials and cost-effectiveness. However, the use of traditional organic liquid electrolytes in sodium battery applications presents significant safety risks, prompting the investigation of solid electrolytes as a more viable solution. Despite their advantages, single solid electrolytes encounter challenges, including low conductivity of sodium ions at room temperature and incompatibility with electrode materials. To overcome these limitations, the researchers develop composite polymer solid electrolytes (CPSEs), which merge the strengths of high ionic conductivity of inorganic solid electrolytes and the flexibility of polymer solid electrolytes. CPSEs are usually composed of inorganic materials dispersed in the polymer matrix. The final performance of CPSEs can be further improved by optimizing the particle size, relative content, and form of inorganic fillers. CPSEs show great advantages in improving ionic conductivity and interface compatibility, making them an important direction for future solid-state sodium battery research. Therefore, this paper summarizes recent advancements in composite solid electrolytes, discusses the impact of their preparation processes on performance, and outlines potential future developments in sodium-ion solid-state batteries.

Modeling solvation dynamics of transition metal redox ion through on-the-fly multi-objective Bayesian-optimized force field

Modeling solvation dynamics and properties is crucial for developing electrolytes for electrochemical energy storage and conversion devices. This work reports an on-the-fly multi-objective Bayesian optimization (OTF-MOBO) method to parameterize force fields for modeling ionic solvation structures, thermodynamics, and transport properties using molecular dynamics simulations. By leveraging solvation-free energy and solvation radii as training data, we employ the data-driven OTF-MOBO algorithm to actively optimize the force field parameters. The modeling accuracy was evaluated in molecular dynamics simulations until the Pareto front in the parameter space was reached through minimized prediction errors in both solvation-free energy and solvation radii. Using transition metal redox ions (Fe3+/Fe2+, Cr3+/Cr2+, and Cu2+/Cu+) in aqueous solution as examples, we demonstrate that simple force fields combining the Lenard-Jones potential and Coulombic potential can achieve relative error below 2% in both solvation free energy and solvation radii. The optimized force fields can be further extrapolated to predict solvation entropy and diffusivities with relative error below 10% compared with experiments.