Accelerated discovery of battery interfaces and materials

At the core of inventing the batteries of the future lies the discovery of high-performance materials and components that enable the creation of batteries with higher energy and power.

BATTERY 2030+ advocates the development of a battery Materials Acceleration Platform (MAP) to reinvent the way we perform battery materials research today. This can be done by combining powerful approaches from high-throughput automated synthesis and characterization, materials and interface simulations, autonomous data analysis and data mining, as well as AI and machine learning.

Interfaces in batteries are arguably the least understood aspect of the battery, even though most of the critical battery reactions occur there, such as dendrite formation, solid electrolyte interphase (SEI) formation, and cathode electrolyte interface (CEI) formation. Building on MAP, BATTERY 2030+ proposes to develop a Battery Interface Genome (BIG) that will establish a new basis for understanding the interfacial processes that govern the operation and functioning of every battery. The accelerated design of battery materials requires the detailed understanding and tailoring of the mechanisms governing interface formation and evolution.

This involves both studying the mechanisms of ion transport through interfaces and, even more challenging, visualising the role of the electron in the interfacial reactions. These processes determine whether the ultra-high-performance batteries developed will be safe to operate and exhibit the long lifetimes that are necessary.

A central aspect will be the development of a shared European data infrastructure capable of performing the autonomous acquisition, handling, and analysis of data from all domains of the battery development cycle. Novel AI-based tools and physical models will utilize the large amounts of data gathered, with a strong emphasis on battery materials and interfaces. The data generated across different length and time scales, using a wide range of complementary approaches, including numerical simulation, autonomous high-throughput material synthesis and characterization, in operando experiments, and device-level testing, will all contribute to new material and battery cell development.

Integrating these two research areas, BIG and MAP, will transform the way we understand and discover new battery materials. Theme I will deliver a transformative increase in the pace of new discoveries for engineering and developing safer, longer-lived, and sustainable ultra-high-performance batteries.

Illustration of the BIG-MAP concept.
Key components of establishing a battery MAP.

The short-, medium and long-term goals for developing BIG-MAP

Theme Accelerated discovery of battery interfaces and materials 
Research area BIG-MAP
Short-term (3 yers)
  • Set up a pan-European interoperable data infrastructure and user interface for battery materials and interfaces. 
  • Establishing integrated experimental and computational workflows.
  • Demonstrating BIG-based hybrid physics- and data-driven models of battery materials. 
  • Deploy autonomous modules and apps for on-the-fly analysis of data characterisation and testing using AI and simulations. 
  • Developing multi-modal high-throughput/high-fidelity interface caracterisation approaches. 
Medium-term (6 years)
  • Fully implement the BIG in MAP to integrate computational modelling, materials autonomous synthesis and characterisation. 
  • Integrate datat from embedded sensors into the discovery and prediction process. 
  • Develop and apply predictive hybrid models for the spatio-temporal evolution of battery interfaces/interphases to perform inverse materials design.
  • Demonstrating transferability of the BIG-MAP approach to novel battery chemistries and interfaces. 
  • Intergating novel experimental and computational techniques targeting the time and length scales of electron localisation, mobility and transfer reactions. 
Long-term (10 years)
  • Demonstrate the integration of manufacturability and recyclability parameters into the materials discovery process. 
  • Integrate battery cell assembly and device-level testing into BIG-MAP.
  • Implement and validate digital twin for ultra-high-throughput testing on the cell level. 
  • Establish and demonstrate full autonomy and chemistry neutrality in the BIG-MAP.
  • Demonstrate a 5-10-fold improvement in the materials discovery cycle and interface performance.