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Discovery of Polymer Electrolytes with Bayesian Optimization and High-Throughput Molecular Dynamics simulations

Antonia S. Kuhn, Jurğis Ruža, KyuJung Jun, Pablo Leon, Rafael Gómez-Bombarelli

Abstract

Polymer electrolytes are critical for safe, high-energy-density solid-state batteries, yet discovering candidates that balance high ionic conductivity with high transference numbers remains a significant challenge. In this work, we develop a high-throughput screening platform that utilizes molecular dynamics simulations to navigate a chemical space of 1.7 million hypothetical polymer electrolyte candidates. Data from previous literature is used to warm-start batch Bayesian optimization for iteratively selecting new polymer electrolytes to evaluate. We iteratively identified, evaluated and analyzed 767 homopolymers as potential candidates. Our results reveal several candidates with transport properties exceeding the benchmark polyethylene oxide (PEO)/LiTFSI system. Crucially, our optimization campaigns for ionic conductivity and Li-diffusivity demonstrate that branched architectures and ketone functional groups significantly enhance ion-hopping mechanisms within the polymer matrix. We provide an in-depth mechanistic comparison of Li vs. Na cation transport and offer our open-source framework to accelerate the discovery of liquid, gel, and multi-cation electrolyte systems.

Discovery of Polymer Electrolytes with Bayesian Optimization and High-Throughput Molecular Dynamics simulations

Abstract

Polymer electrolytes are critical for safe, high-energy-density solid-state batteries, yet discovering candidates that balance high ionic conductivity with high transference numbers remains a significant challenge. In this work, we develop a high-throughput screening platform that utilizes molecular dynamics simulations to navigate a chemical space of 1.7 million hypothetical polymer electrolyte candidates. Data from previous literature is used to warm-start batch Bayesian optimization for iteratively selecting new polymer electrolytes to evaluate. We iteratively identified, evaluated and analyzed 767 homopolymers as potential candidates. Our results reveal several candidates with transport properties exceeding the benchmark polyethylene oxide (PEO)/LiTFSI system. Crucially, our optimization campaigns for ionic conductivity and Li-diffusivity demonstrate that branched architectures and ketone functional groups significantly enhance ion-hopping mechanisms within the polymer matrix. We provide an in-depth mechanistic comparison of Li vs. Na cation transport and offer our open-source framework to accelerate the discovery of liquid, gel, and multi-cation electrolyte systems.
Paper Structure (6 sections, 2 equations, 5 figures)

This paper contains 6 sections, 2 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. Results and Discussion
  4. Conclusion
  5. Acknowledgment
  6. Code and Data availability

Figures (5)

  • Figure 1: Workflow of our active learning high-throughput pipeline. Experimental values from literature are used for a warm start of the Gaussian Process Regression (GPR) model. The polymers are embedding with the MolFormer model and this space is clustered to enforce larger exploration of the chemical space. The HiTPoly workflow is employed to simulated the sampled polymers and obtain ionic conductivity to retrain the GPR model and sample new candidates for multiple batches.
  • Figure 2: (a) Performance comparison of our warm start GP model against a no warm-up GP and random search for 25 generations. (b) The first two principal components of the total search space in gray with the evolution of the convex hull by batch of training. (c) The evolution of convex hull area and number of convex hull vertices as a function of generation run for the first 5 principal component dimensions. (d) The evolution of the best ionic conductivity for each batch with boxes representing the mean and variance of the ionic conductivity for the whole batch.
  • Figure 3: (a) Schematic of the different optimization targets during this campaign. (b) Parallel coordinates of the five polymers with the highest performance for each optimization property decomposed by the four main components of the properties. (c) All pairwise relationships between the four properties of interest. Diagonal plots show the distribution of each property, lower triangle shows the pairwise scatter plots and the upper triangle shows the bivariate distributions using kernel density estimation.
  • Figure 4: (a) Enrichment of different types of chemical groups for the various optimization targets in the top 5% best samples for each target compared to the prevalence of those chemical groups in all of the tested samples. (b) Enrichment evolution for linear branches (blue, right axis), conjugated groups (dashed line), ketone groups (dotted line), aromatic groups (dotted-dashed line) across the 45 batches compared to prevalence of those groups in the total dataset.
  • Figure 5: (a) 11 polymers with varying structures selected from the top 50 polymers with the highest ionic conductivity. (b) Ionic conductivity at 393 K against the activation energy of ionic conductivity for Li (orange points) and Na (green points). (c) Transference number against the ratio of coordination number and coordination shell radius both at 393 K. (d) Simulated NE Li diffusivity against CRU diffusivity both at 393 K. (e) Sankey diagram of the decomposition of the various transport mechanisms. (1) Polymer chains entering or leaving solvation shell (top yes, bottom no). (2) Anions entering or leaving the solvation shell (top yes, bottom no). (3) Change in coordinating atoms from the majority solvating polymer chain (top change in coordinating atoms, bottom no change). (f) The contributions of each of the transport mechanisms to the overall cation ionic conductivity for each of the polymers at 393 K.