Section for Autonomous Materials Discovery

We focus on transforming the discovery of sustainable energy materials by developing chemistry-agnostic Materials Acceleration Platforms that integrate the full research cycle. Our approach combines predictive workflows and advanced software capable of describing materials under realistic operating conditions with machine learning methods that bridge temporal and spatial scales. These digital capabilities are tightly coupled with autonomous synthesis and on-the-fly characterization, enabling closed-loop, self-improving experimentation. By unifying data, models, and experiments, we accelerate the identification of next-generation battery materials and electrocatalysts for Power2X applications. At the same time, we create new digital solutions that reshape how research is conducted and taught.