# Navigating Ternary Doping in Li‐ion Cathodes With Closed‐Loop Multi‐Objective Bayesian Optimization

## TL;DR

This paper introduces a machine learning approach to efficiently explore complex battery material compositions, optimizing multiple electrochemical properties simultaneously.

## Contribution

A closed-loop, multi-objective Bayesian optimization workflow is developed for ternary doping in Li-ion cathodes, enabling efficient exploration of a vast composition space.

## Key findings

- A deep learning model pretrained on the Materials Project database and fine-tuned on high-throughput data optimizes four electrochemical properties.
- Only 125 random compositions and 63 predicted samples are needed to achieve significant improvements in battery metrics.
- The best composition increases the composite figure of merit up to five times compared to the undoped system.

## Abstract

To further improve secondary battery materials, we are increasingly exploring highly complex composition spaces in attempts to optimize multiple properties simultaneously. While our past work has done this in systematic manners using high‐throughput experimentation, the exponential increase in the search space with triple doping makes grid search prohibitively expensive. Here, we demonstrate a closed‐loop, multi‐objective machine learning approach to guide the high‐throughput workflow to efficiently navigate a space with approximately 14 million unique combinations. The test system is LiCoPO4, which we have previously explored using systematic codoping that was effective in optimizing one property only: energy density. To learn multiple electrochemical metrics, we first pretrain a set transformer on the public Materials Project database as a feature extractor, then attach a multi‐task Gaussian process head and finetune the entire model on our high‐throughput data. Through 3 rounds of active learning, we demonstrate that with a very small number of samples (as few as 125 random compositions and 63 predicted), we are able to simultaneously optimize four key electrochemical properties. Relative to the undoped system, the best composition raises our composite figure of merit up to five times. This establishes an end‐to‐end workflow for accelerated battery materials design to be used in the rapidly growing field of autonomous materials discovery.

The search for advanced battery materials is pushing us into highly complex composition spaces. Here, a space with about 14 million unique combinations is efficiently explored using high‐throughput experimentation guided by Bayesian optimization with a deep kernel trained on both the Materials Project database and our data. The outcome is a cathode material optimized for four important battery metrics.