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ion.ai names the physics AI is accelerating: ionic conduction is the working principle of batteries, and AI compressed a battery-materials search from years toward days. Microsoft and the Department of Energy's PNNL used AI to screen 32 million candidate materials and surface a new solid-state electrolyte using roughly 70% less lithium, narrowing the field to 18 promising candidates in 80 hours before synthesis into a prototype.
AI x Materials
32 million candidates to a working battery
Microsoft said its AI and HPC workflow screened more than 32 million candidate materials and helped identify a solid-state electrolyte using roughly 70% less lithium, later synthesized and prototyped by PNNL.
AI x Materials
32 million candidates to a working battery
Microsoft's AI and HPC pipeline screened 32 million inorganic materials, and PNNL synthesized the leading candidate into a functioning prototype battery.
Lithium
70% less lithium via AI-found chemistry
The discovered lithium-sodium-yttrium-chloride electrolyte contains about 70% less lithium than leading solid-state electrolytes while showing viable ionic conductivity.
Speed
Discovery in weeks, not years
The candidate list went from millions to 18 in 80 hours, with the full discovery-to-battery cycle completed in under nine months.
Context for ion.ai
ionic conductivity
AI materials discovery
energy storage
scientific brand
The property AI-designed electrolytes optimize for, measured directly on the PNNL-synthesized material.
The generalizable pattern is AI screening replacing trial-and-error across battery, catalyst, and semiconductor chemistry, as in the Microsoft and PNNL materials pipeline.
Lithium-ion economics and supply constraints make ion-level innovation strategic, illustrated by a candidate using much less lithium.
Short, technical, and pronounceable, with evidence tied to AI-accelerated battery chemistry.