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High-Throughput Screening of 6858 Compounds for Zinc-Ion Battery Cathodes via Hybrid Machine Learning Optimization
Yakubu Sani Wudil1,2, Mohammed A Gondal2,3, Mohammed A Al-Osta1,4
1Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
ACS Applied Materials & Interfaces
|February 4, 2025
Summary
Machine learning accelerates the discovery of cathode materials for zinc-ion batteries. This framework identifies 18 promising electrodes by predicting key properties and applying screening criteria for enhanced energy storage.
Area of Science:
- Materials Science
- Electrochemistry
- Computational Chemistry
Background:
- Zinc-ion batteries (ZIBs) offer a sustainable alternative to lithium-ion batteries.
- Discovering efficient and stable cathode materials is crucial for ZIB advancement.
- Data-driven approaches are needed to accelerate materials discovery.
Purpose of the Study:
- To develop a machine learning (ML) framework for exploring novel cathode materials for ZIBs.
- To predict electrochemical properties and screen potential candidates from a large dataset.
- To accelerate the identification of high-performance and stable ZIB cathode materials.
Main Methods:
- Utilized a dataset of 6858 zinc-containing compounds from the Materials Project (MP) database.
- Employed a two-step ML approach with transfer learning to impute missing electrochemical data.
- Developed hybrid models (SSA-LGBM and HHO-DNN) after feature reduction via principal component analysis.
Main Results:
- Successfully predicted key properties like average voltage and gravimetric capacity for 62 potential electrodes.
- Screened these candidates based on voltage, capacity, conductivity, safety, stability, cost, and abundance.
- Identified 18 promising cathode materials for zinc-ion battery applications.
Conclusions:
- The ML framework significantly accelerates the discovery of efficient and stable cathode materials for ZIBs.
- This approach provides a robust method for future materials exploration in various battery technologies.
- Enables more sustainable and high-performance energy storage solutions.
