Aionics, a leading developer of AI-powered materials discovery technologies, announced the launch of its Artificial Molecular Intelligence (AMI)platform-a first-of-its-kind capability for real-time, autonomous formulation screening. This breakthrough marks a new era in chemical R&D, offering transformative speed and scale in the design of high-performance battery electrolytes and a wide range of other chemical formulations.
With AMI, Aionics introduces a new class of artificial intelligence purpose-built for the staggering combinatorial complexity of chemical mixtures and their integration into real-world devices. Unlike conventional AI tools that analyze molecules in isolation, AMI evaluates full formulations—including molecular interactions, interface behaviors, and commercial constraints—in milliseconds. The platform currently screens over 3,000 formulations per second, enabling rapid optimization across a library of more than 10 billion molecules, including both commercially available compounds and novel molecules created through generative AI.
“Ami is a transformative shift in materials development. We’re no longer bound by trial-and-error or narrow intuition-led approaches,” said Austin Sendek, CEO of Aionics. “We’ve created a system that can autonomously explore chemical space and design formulations optimized for performance and practicality-at a pace the industry has never seen before. Imagine being able to screen any and all molecules for any and all applications. This represents a profound, exciting shift for the chemicals and materials sector.”
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Introducing the Ami Platform
To commercialize AMI, Aionics has built the Ami platform (pronounced “Amy”), an integrated platform combining high-speed simulation, machine learning, and lab feedback in a closed-loop architecture. The Ami platform allows customers to specify performance targets and then rapidly returns optimized formulations that meet both technical and commercial criteria.
Ami addresses the two core challenges holding back formulation design today: reliable computational models for predicting key properties of interest do not exist for many key application areas, and the breadth of chemical space is simply too large to ever explore without AI acceleration. This is especially true in the space of battery electrolytes, where most commercialized Li-ion electrolytes are made up of different combinations of the same 11 molecules, while there over 10 billion unexplored molecules.
SOURCE: GlobeNewswire