- Researchers have discovered new materials for multi-charged ion batteries using artificial intelligence, using magnesium, calcium, aluminum and zinc.
- The AI model identified five new porous transition metal oxide structures with large open channels that are ideal for ion transport.
- This technique combines two AI tools that allow thousands of crystal structures to be searched significantly faster than traditional laboratory experiments.
Researchers are tackling major challenges in energy storage
The development of next-generation energy storage systems requires discovering new materials that can process multi-charged ions. Transition metal oxides are promising because of their structural versatility, high ionic conductivity, and their ability to accommodate multiple charge carriers.
Unlike traditional lithium-ion batteries, which rely on lithium ions for just one positive charge, a multi-charged ion battery uses elements in which ions carry two or three positive charges. This means that a large amount of ion batteries can potentially store significantly more energy.
However, the larger the size and the larger the charge of the multi-charged ions, the more difficult it is to efficiently deal with battery materials.
AI models explore thousands of structures
To overcome these hurdles, the NJIT team has developed a new dual AI approach: Crystal Diffusion Mutation AutoEncoder (CDVAE) and a fine-tuned major language model (LLM). Together, these AI tools rapidly investigated thousands of new crystal structures.
The CDVAE model was trained on a wide range of datasets of known crystal structures, allowing us to propose completely new materials with diverse structural possibilities. Meanwhile, LLM was tailored to focus on materials closest to thermodynamic stability, which is important for practical synthesis.
This study used 44,411 inorganic structures based on transition metal oxide materials, including binary, ternary, Quaternary, Qus and Senate compositions. The ternary transition metal oxides comprised approximately 26,393 data points, and the Senate transition metal oxides were underestimated with just 37 entries.
Successful results with new structure
The CDVAE model generated 10,000 structures that undergo a rigorous screening and validation process. To apply filters to structural and constitutive validity and ensure uniqueness, 8,203 out of 10,000 structures passed the initial screening.
After applying property-based filters, 42 structures were obtained from the CDVAE approach. The selection included five oxygen-containing structures and 37 oxygen-free structures. Of these, 21 structures matched existing entries in the Material Project Database, but provided new configurations with differences in stoichiometry, lattice parameters, or space groups. The remaining 21 structures were completely novel.
The LLM model also generated 10,000 structures. After applying composition, structural validity, and uniqueness checks, 1,087 structures remained. After filtering, only 13 structures passed the standard.
Quantum mechanical simulation verification
The team used quantum mechanical simulations and stability tests to verify the structures generated in AI. For structural relaxation, DFT relaxation was applied to all 42 filtered structures from the CDVAE model, and the researchers were able to relax 40 of these structures. All structures from LLM have been successfully optimized.
The LLM model produces 46.15% stable structures, while the CDVAE model produces only 15% stable structures. An inverse trend is observed for metastable materials with LLM producing a metastable structure of 23.08%, and the CDVAE model results in a metastable composition of 40%.
The five TMO-based structures generated by the CDVAE model include a large, open tunnel framework designed to promote ion transport by accommodating multi-charged ions. Three of the five produced compositions are also present in the Materials Project database, but the stoichiometry ratio is different.
Next Steps for Practical Applications
Researchers plan to work with the lab to integrate and test materials for AI designs. This method establishes a rapid and scalable approach to exploring advanced materials from electronics to clean energy solutions without extensive trial and error.
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