Design of Polymer Blend Electrolytes through a Machine Learning Approach

Strategies for Enhancing the Stability of Lithium Metal Anodes in Solid-State Electrolytes

The current commercially used anode material, graphite, has a theoretical capacity of only 372 mAh/g, leading to a relatively low energy density. Lithium (Li) metal is a promising candidate as an anode for enhancing energy density; however, challenges related to safety and performance arise due to Li's dendritic growth, which needs to be addressed. Owing to these critical issues in Li metal batteries, all-solid-state lithium-ion batteries (ASSLIBs) have attracted considerable interest due to their superior energy density and enhanced safety features. Among the key components of ASSLIBs, solid-state electrolytes (SSEs) play a vital role in determining their overall performance. Various types of SSEs, including sulfides, oxides, and polymers, have been extensively investigated for Li metal anodes. Sulfide SSEs have demonstrated high ion conductivity; however, dendrite formation and a limited electrochemical window hinder the commercialization of ASSLIBs due to safety concerns. Conversely, oxide SSEs exhibit a wide electrochemical window, but compatibility issues with Li metal lead to interfacial resistance problems. Polymer SSEs have the advantage of flexibility; however their limited ion conductivity poses challenges for commercialization. This review aims to provide an overview of the distinctive characteristics and inherent challenges associated with each SSE type for Li metal anodes while also proposing potential pathways for future enhancements based on prior research findings.

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Data-Driven Prediction of Janus/Core-Shell Morphology in Polymer Particles: A Machine-Learning Approach

The majority of research on Janus particles prepared by solvent evaporation-induced phase separation technique uses models based on interfacial tension or free energy to predict Janus/core-shell morphology. Data-driven predictions, in contrast, utilize multiple samples to identify patterns and outliers. Using machine-learning algorithms and explainable artificial intelligence (XAI) analysis, we developed a model based on a 200-instance data set to predict particle morphology. As model features, simplified molecular input line entry system syntax identifies explanatory variables, including cohesive energy density, molar volume, the Flory-Huggins interaction parameter of polymers, and the solvent solubility parameter. Our most accurate ensemble classifiers predict morphology with an accuracy of 90%. In addition, we employ innovative XAI tools to interpret system behavior, suggesting phase-separated morphology to be most affected by solvent solubility, polymer cohesive energy difference, and blend composition. While polymers with cohesive energy densities above a certain threshold favor the core-shell structure, systems with weak intermolecular interactions favor the Janus structure. The correlation between molar volume and morphology suggests that increasing the size of polymer repeating units favors Janus particles. Additionally, the Janus structure is preferred when the Flory-Huggins interaction parameter exceeds 0.4. XAI analysis introduces feature values that generate the thermodynamically low driving force of phase separation, resulting in kinetically stable morphologies as opposed to thermodynamically stable ones. The Shapley plots of this study also reveal novel methods for creating Janus or core-shell particles based on solvent evaporation-induced phase separation by selecting feature values that strongly favor a given morphology.

Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery

Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity.

Machine learning strategies for the structure-property relationship of copolymers

Establishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers.

Charge Transport and Glassy Dynamics in Blends Based on 1-Butyl-3-vinylbenzylimidazolium Bis(trifluoromethanesulfonyl)imide Ionic Liquid and the Corresponding Polymer

Charge transport, diffusion properties, and glassy dynamics of blends of imidazolium-based ionic liquid (IL) and the corresponding polymer (polyIL) were examined by Pulsed-Field-Gradient Nuclear Magnetic Resonance (PFG-NMR) and rheology coupled with broadband dielectric spectroscopy (rheo-BDS). We found that the mechanical storage modulus (G′) increases with an increasing amount of polyIL and G′ is a factor of 10,000 higher for the polyIL compared to the monomer (GIL′= 7.5 Pa at 100 rad s⁻¹ and 298 K). Furthermore, the ionic conductivity (σ0) of the IL is a factor 1000 higher than its value for the polymerized monomer with 3.4×10−4 S cm⁻¹ at 298 K. Additionally, we found the Haven Ratio (HR) obtained through PFG-NMR and BDS measurements to be constant around a value of 1.4 for the IL and blends with 30 wt% and 70 wt% polyIL. These results show that blending of the components does not have a strong impact on the charge transport compared to the charge transport in the pure IL at room temperature, but blending results in substantial modifications of the mechanical properties. Furthermore, it is highlighted that the increase in σ0 might be attributed to the addition of a more mobile phase, which also possibly reduces ion-ion correlations in the polyIL.

Machine Learning-Assisted Design of Material Properties

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Bayesian optimization for goal-oriented multi-objective inverse material design

Bayesian optimization (BO) can accelerate material design requiring time-consuming experiments. However, although most material designs require tuning of multiple properties, the efficiency of multi-objective (MO) BO in time-consuming experimental material design remains unclear, due to the complexity of handling multiple objectives. This study introduces MO BO method that efficiently achieves predefined goals and shows that by focusing on achieving the goals, BO can efficiently accelerate realistic MO design problems with small efforts. Benchmarks showed that the proposed BO method dramatically reduced the number of experiments needed to achieve goals relative to a baseline method. Virtual MO inverse design experiments with realistic material design problems were also performed, during which the proposed method could achieve goals within only around ten experiments in average and showed over 1000-fold acceleration relative to the random sampling for the most difficult case. The introduction of goal-oriented BO will precede real-world application of BO.