The development of new energy technologies, essential for transitioning to a sustainable
future, relies on the discovery of new materials. Over the past decades, materials simulations
have significantly accelerated the discovery process, complementing experimental
approaches. These simulations offer unique insights into the fundamental mechanisms that
drive material behavior. Additionally, they can predict material properties and elucidate the
relationship between atomic structures and their properties, thereby enabling a rational design
of materials with specific characteristics. Despite their success, the discovery process has
traditionally been slow, requiring iterative cycles between theoretical predictions and
experimental verifications until optimal materials are identified, synthesized, and tested in real
devices. This paradigm has recently been broken by the creation of Materials Acceleration
Platforms (MAPs), where AI-orchestrated collaboration between AI-accelerated materials
simulations and self-driving laboratories enables closed-loop materials discovery.
In my talk, I will first discuss the development of a technology-agnostic, autonomous, and
standardized modelling framework and its integration into a MAP. The foundation of this
infrastructure is a dynamic workflow management system capable of orchestrating calculations of thermodynamic and kinetic properties, which play a fundamental role for many energy technologies. Within this framework, we have established the first autonomous workflow to discover new electrodes and solid-state electrolytes for the batteries of the future. Beyond batteries, this technology-agnostic workflow can be applied to discover new materials for a wide range of next-generation energy technologies, from fuel cells to photovoltaics. While workflows are commonly used for bulk materials, the investigation of interfaces often relies on manual, time-consuming methods based on trial and error. I will describe our efforts to implement autonomous workflows for interfaces and integrate them with the design of bulk
structures, using our work on understanding and controlling the solid/electrolyte interface in
Li-ion batteries as an example. To fully realize the potential of a MAP, a seamless data
infrastructure is required, which is capable of handling curated data and metadata from
multiple sources and with varying levels of fidelity. At the end of my talk, I will present our
approach to developing such a data infrastructure. This includes achieving complete
interoperability of computational workflows and electronic laboratory notebooks from different
sources. These involve various simulation engines, time and length scales, and automated
data collection and metadata annotation in an ontology-compliant format.