Materials Acceleration Platform
Materials discovery and development is a fundamental need that crosscuts the entire clean energy technology portfolio, ranging from energy generation and storage to delivery and end-use.
Advanced materials are the foundation of nearly every clean energy innovation, particularly for emerging battery technologies. Relying on existing trial-and-error based development processes, the discovery of novel high-performance battery materials and cell designs entails considerable effort, expense, and time – traditionally over 10 years from initial discovery to commercialization.
The BATTERY 2030+ Roadmap outlines a radically new path for accelerated development of ultra-high performance, sustainable and smart batteries, which hinges on the development of faster and more cost-effective methods for battery discovery and manufacturing – the Materials Acceleration Platform (MAP).
The MAP will enable closed-loop materials discovery and development through the use of AI to orchestrate data acquisition and analysis from multi-scale computer simulations, experiments and testing. This also includes the development of autonomous high throughput synthesis robotics and experiments utilizing the Europe large-scale synchrotron and neutron facilities. The MAP will be integrated with the Battery Interface Genome (BIG) to create an infrastructure that is modular and versatile, in order to be able to accommodate all emerging battery chemistries, materials compositions, structures and interfaces.
Realization of each of the core elements of the conceptual battery MAP framework entails significant innovation challenges and the development of key enabling technologies. Combined, these enabling technologies permit completely new battery development strategies, by inverse designing and tailoring materials, processes and devices on demand and finally to couple all elements of the MAP to enable AI-orchestrated autonomous discovery of battery materials.
The realization of these technologies will enable discovery of new battery materials and cells at unprecedented speeds. Successful integration of computational materials design, AI, modular and autonomous synthesis robotics and advanced characterization will lay the foundation for accelerating the traditional materials discovery process dramatically through creation of “self-driving” laboratories capable of designing and synthesizing novel battery materials, and to orchestrate and interpret experiments on-the-fly in a closed-loop materials discovery process. Its implementation constitutes a quantum leap forward in materials design, which can be achieved only through the integration of all relevant European expertise.
The autonomous BIG-MAP: The forward vision is to develop a versatile and chemistry neutral framework capable of achieving a 10-fold increase in the rate of discovery of novel battery materials and interfaces. The backbone of this vision is the “Battery Interface Genome - Materials Acceleration Platform (BIG-MAP)”, which will ultimately enable inverse design of ultra-high performance battery materials and interfaces/interphases, and be capable of integration cross-cutting aspects like manufacturability and recyclability directly into the discovery process.
The full BIG-MAP will also rely heavily on the direct integration of the insights developed in BIG and the novel concepts developed in the area of sensors and self-healing.
In order to achieve this ambitious goal, a number of short-, medium and long-term objectives have been identified:
In short term:
- Autonomous analysis of experimental and simulation results using AI
- A shared BIG-MAP data/modelling infrastructure for closed loop discovery
- Data-driven models guided by physical understanding (hybrid models)
- Computational strategies to identify and pass features between scales
In medium term:
- Fully autonomous platform capable of integrating computational modelling, materials synthesis and characterization
- Inverse design of battery materials and interphases demonstrated
- Integrate sensing and self-healing integrated in BIG-MAP
In long term:
- Fully autonomous and automated platform, integrating computational modelling, material synthesis and characterisation, battery cell assembly and device-level testing
- Manufacturability and recyclability included in the materials discovery process
- Digital twin for ultra-high throughput testing on cell level implemented and validated