Imperial College
AMLearn can be used to accelerate battery innovation with AI and automation. Explore how machine learning and automation can be applied to fast-track the development of next-generation battery materials
AMLearn can be used to accelerate battery innovation with AI and automation. Explore how machine learning and automation can be applied to fast-track the development of next-generation battery materials
We recently had the privilege of working with the incredible team at Imperial College London’s DIGIBAT group, training their researchers on how to build self-driving labs using our AMLearn platform.
In the session, we explored how machine learning and automation can be applied to fast-track the development of next-generation battery materials. Using their existing Chemspeed system, we demonstrated how AMLearn can integrate seamlessly into their lab workflows, pulling experimental data to support real-time decision making and optimisation.
As a major milestone, the DIGIBAT team prepared an internal demo to showcase how AMLearn can be used to accelerate battery R&D, laying the foundation for autonomous experimentation.
We’re excited to see how Imperial’s researchers continue pushing the boundaries of materials discovery using AMLearn.
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