High-Throughput
Screening of 6858 Compounds for Zinc-Ion
Battery Cathodes via Hybrid Machine Learning Optimization
Posted on 2025-02-05 - 01:29
This
work presents a machine-learning framework to explore cathode
materials for zinc-ion batteries from a data set of 6858 zinc-containing
compounds. Utilizing the extensive Materials Project (MP) database,
we employed a two-step machine learning (ML) approach that uses transfer
learning to compensate for missing electrochemical properties. Initially,
a random forest regressor was used to fill in missing features in
the zinc compounds, harnessing the full battery explorer in predictions.
Two hybrid models were then developed: the sparrow search algorithm-light
gradient boosting machine (SSA-LGBM), and Harris Hawk optimization-deep
neural networks (HHO-DNN). The data set contains 107 feature vectors,
which were minimized through principal component analysis. These features
include descriptors related to structural, chemical, and electronic
properties. Both models were trained using the 4351 known battery
compounds from MP to predict key properties such as average voltage
and gravimetric capacity. After initial prediction of 62 potential
electrodes, further screening criteria were applied to identify 18
promising electrodes based on their voltage, specific capacity, electronic
conductivity, safety, stability, cost, and abundance. The validation
of our approach was carried out by applying the models to known cathode
materials, verifying the accuracy of the predictions. This innovative
approach significantly accelerates the discovery of efficient and
stable cathode materials for zinc-ion batteries, paving the way for
more sustainable and high-performance energy storage solutions. This
method also provides a robust framework for future materials exploration
across various battery technologies.
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Wudil, Yakubu Sani; Gondal, Mohammed A.; Al-Osta, Mohammed A. (2025). High-Throughput
Screening of 6858 Compounds for Zinc-Ion
Battery Cathodes via Hybrid Machine Learning Optimization. ACS Publications. Collection. https://doi.org/10.1021/acsami.4c18556