It took just 80 hours, rather than decades, to identify a potential new solid electrolyte using a combination of supercomputing and AI.
In the hunt for new materials, scientists have traditionally relied on tinkering in the lab, guided by intuition, with a hefty serving of trial and error.
The researchers targeted a coveted type of battery material: a solid electrolyte. An electrolyte is a material that transfers ions — electrically charged atoms — back and forth between a battery’s electrodes. In standard lithium-ion batteries, the electrolyte is a liquid. But that comes with hazards, like batteries leaking or causing fires. Developing batteries with solid electrolytes is a major aim of materials scientists.
That left the researchers with 23 candidates, five of which were already known. Researchers at PNNL picked a material that looked promising — it was related to other materials that the researchers knew how to make in the lab, and it had suitable stability and conductivity. Then they set to work synthesizing it, eventually fashioning it into a prototype battery. And it worked.
In the new work, the researchers created a series of AI models that could predict different properties of a material, based on training data from known materials. The AI architecture is a type known as a graph neural network, in which a system is represented as a graph, a mathematical structure composed of “edges” and “nodes.” This type of model is particularly suited for describing materials, as the nodes can represent atoms, and the edges can represent bonds between the elements.
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