🔋 AI creates new battery materials that could revolutionize energy storage

• Researchers have used artificial intelligence to discover new materials for multivalent-ion batteries using magnesium, calcium, aluminum, and zinc.
• The AI models identified five new porous transition metal oxide structures with large open channels ideal for ion transport.
• The technique combines two AI tools that together can explore thousands of crystal structures significantly faster than traditional laboratory experiments.

Researchers tackle a major challenge in energy storage

The development of next-generation energy storage systems requires discovering new materials that can handle multivalent ions. Transition metal oxides are promising due to their structural versatility, high ionic conductivity, and ability to accommodate multiple charge carriers.

Unlike traditional lithium-ion batteries, which rely on lithium ions with just one positive charge, multivalent-ion batteries use elements whose ions carry two or even three positive charges. This means multivalent-ion batteries can potentially store significantly more energy.

However, the larger size and greater electrical charge of multivalent ions make them challenging to accommodate efficiently in battery materials.

AI models explore thousands of structures

To overcome these hurdles, the NJIT team developed a novel dual-AI approach: a Crystal Diffusion Variational Autoencoder (CDVAE) and a fine-tuned Large Language Model (LLM). Together, these AI tools rapidly explored thousands of new crystal structures.

The CDVAE model was trained on extensive datasets of known crystal structures, enabling it to propose completely novel materials with diverse structural possibilities. Meanwhile, the LLM was tuned to focus on materials closest to thermodynamic stability, crucial for practical synthesis.

The study used 44,411 inorganic structures based on transition metal oxide materials, including binary, ternary, quaternary, quinary, and senary configurations. Ternary transition metal oxides constituted approximately 26,393 data points, while senary transition metal oxides were underrepresented with only 37 entries.

Successful results with new structures

The CDVAE model generated 10,000 structures that underwent rigorous screening and validation processes. After applying filters for structural and compositional validity and to ensure uniqueness, 8,203 out of the 10,000 structures passed the initial screening.

After applying property-based filters, 42 structures were retrieved from the CDVAE approach. The selection included 5 oxygen-containing structures and 37 oxygen-free structures. Of these, 21 structures matched existing entries in the Materials Project database but offered new configurations with differences in stoichiometry, lattice parameters, or space groups. The remaining 21 structures were entirely novel.

The LLM model also generated 10,000 structures. After applying compositional, structural validity, and uniqueness checks, 1,087 structures remained. After filtering, only 13 structures passed the criteria.

Validation through quantum mechanical simulations

The team validated their AI-generated structures using quantum mechanical simulations and stability tests. For structural relaxation, DFT relaxation was applied to all 42 filtered structures from the CDVAE model, and researchers were able to successfully relax 40 of these structures. All structures from the LLM were successfully optimized.

The LLM model generates 46.15 percent stable structures, while the CDVAE model generates only 15 percent stable structures. A reverse trend is observed for metastable materials, where the LLM yields 23.08 percent metastable structures, while the CDVAE model results in 40 percent metastable composition.

The five TMO-based structures generated by the CDVAE model have large, open-tunnel frameworks designed to facilitate ion transport by accommodating multivalent ions. Three of the five generated compositions also exist in the Materials Project database, though with different stoichiometric ratios.

Next steps toward practical application

The researchers plan to collaborate with experimental laboratories to synthesize and test their AI-designed materials. The method establishes a rapid, scalable approach for exploring advanced materials from electronics to clean energy solutions without extensive trial and error.

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