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AI technology could improve energy storage

Scientists at Cornell University in the US have identified almost 63,000 materials for fast-ion conductors, which are vital components of solid-state batteries.

Researchers used AI framework called IonNet to search around 4,500 stable compounds, which led them to identify 87 potential fast-iron connectors.

They then expanded to five million possible materials to predict how effectively lithium ions can move through solids using its chemical composition.

When solid-state batteries are developed it allows for further advancements in energy storage.

Fengqi You, PhD, a professor at Cornell University said: “Most AI models for materials require reliable crystal structures, which are often unavailable for new or experimentally reported compounds.”

“IonNet predicts ion mobility from chemical composition, even without precise crystal structures.”

In a separate study involving Professor You’s lab and researchers from the University of Puerto Rico–Río Piedras, scientists demonstrated that changing the chemical environment around ions could increase the voltage of concentration batteries.

These unconventional batteries generate electricity from differences in electrolyte composition rather than using different electrode materials. These were previously assumed to produce less than 0.06 volts (V).

However the new research pushed zinc and copper-based concentration batteries to 0.7V.

Both studies could shape the future of energy storage and save significant computing resources by filtering out less promising materials before simulations or lab experiments start.

Energy Live News
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This article first appeared on Energy Live News, an award winning news service. Their mission is to give you balanced news, analysis, commentary of energy from their dedicated team of quality journalists and production staff.
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