AI Is Designing The Next Decade of Energy Storage

The traditional path to discovering a new battery material involves chemists coming up with a hypothesis, synthesizing a compound, and then testing it. The result informs the next hypothesis, and so on and so forth. 

If they are efficient, they might test a few dozen compounds per year. The discovery of lithium-ion batteries had taken decades of this iterative work.

In 2025, Microsoft and the Pacific Northwest National Laboratory approached the problem differently. They used AI to screen 32 million theoretical inorganic materials in 80 hours. Conventional lab work would have required over two decades to evaluate the same materials. From those millions, they identified 18 promising candidates. One of them, a solid-state electrolyte temporarily named N2116, showed the potential to reduce lithium use by up to 70% (Enertherm, 2025).

From AI prediction to working battery prototype, it would take about nine months.

The constraint in battery designing has never been chemistry, but the speed at which ideas can be tested. AI has eliminated that constraint.

AI models predict material properties such as ionic conductivity, electrical conductivity, and stability, without requiring physical synthesis or testing. A chemist filters the AI-selected candidates for feasibility and promise. The lab synthesizes only the most promising materials. Then the test results feed back into the model, which refines its predictions for the upcoming iteration (Enertherm, 2025).

Lithium-ion batteries face supply constraints, Lithium mining creates environmental damage, and even the refining capacity is limited. If AI can identify alternatives built from abundant elements such as magnesium, calcium, aluminum, or zinc, the economics of energy storage completely change.

Researchers at the New Jersey Institute of Technology showed this using generative AI to discover new porous materials for multivalent-ion batteries (SciTechDaily, 2025). These batteries use elements with multiple positive charges, potentially offering higher energy storage than lithium. The team's approach combined a Crystal Diffusion Variational Autoencoder with a large language model to rapidly identify five novel porous transition metal oxide structures.

None of these materials required researchers to understand the chemistry in advance. The AI found structures that work before anyone could predict them using traditional reasoning. The researchers then validated the predictions through quantum mechanical simulations and stability tests (SciTechDaily, 2025).

AI greatly boosts the speed and scale of exploration. Traditional research explores a few hundred compounds. AI-augmented research explores millions. The statistical probability of finding a breakthrough scales highly with exploration breadth. 

The same approach works for any materials discovery problem. Whether it be catalysts for carbon capture, semiconductors for quantum computing, polymers for sustainable manufacturing, wherever the search space is large and the evaluation is expensive, AI accelerates discovery.

This creates a new dependency. The speed of materials science innovation now depends on access to technology and data, not just chemical intuition. Organizations with the compute resources and data access to run these simulations at scale will discover materials faster. They will commercialize faster. They will dominate markets faster.

However, the chemists do not become obsolete. They become more selective. Instead of testing dozens of ideas, they select from AI-generated candidates and focus on the most promising ones. Their expertise becomes evaluation and validation rather than generation and testing.

Discovery becomes a collaboration between human intuition and machine exploration. The human decides what properties matter and filters impossible ideas. The machine explores the space humans cannot see.

Lithium supply is already strained while energy demand is accelerating. If AI can identify battery materials built from abundant elements with performance comparable to lithium-ion systems, it accelerates the transition away from fossil fuels. 

The next decade of energy storage will not be discovered by chemists working through methodical trials. It will be discovered by AI exploring solution spaces humans could never exhaustively search, with chemists validating and optimizing the results.