Battery Interface Genome
Radically new approaches are needed to accelerate the discovery and development of ultrahigh-performance battery materials and interfaces.
Past experience has shown that when developing new battery chemistries or introducing new functionalities into an existing battery technology, interfaces hold the key to exploit the full potential of the electrode materials and to develop ultra-high performance, sustainable and smart batteries.
The European battery research and development landscape consists of a multitude of research institutions, laboratories and industries, many of which pursue complementary approaches to tackle this challenge at a local scale. BATTERY 2030+ will bring together this expertise with cross-sectoral competences, industrial partners and end-users to establish the Battery Interface Genome (BIG) and accelerate the development of radically new battery technologies.
Current research methodology relies largely on incremental advances made at a local scale, which are not pertinent to tackle the ambitious challenges outlined in this Roadmap. Thee Materials Acceleration Platform (MAP) will provide the infrastructural backbone to accelerate our findings, while BIG will develop the necessary understanding to control the formation and dynamics of the crucial interfaces and interphases, which are limiting battery performance.
Furthermore, as it remains open which will be the winning battery technologies for large scale and grid storage, for mobility, et cetera, BIG will be highly adaptive to different chemistries, materials and designs, starting from (beyond) state-of-the-art in Li-ion technology, where substantial data and insights are available for the training of the models, to emerging and radically new chemistries.
Batteries comprise not only an interface between the electrode and the electrolyte, but a number of other important interfaces, e.g. between the current collector and the electrode or between the active material and the additives such as conductive carbon and/or binder, etc. Realizing this, any globally leading approach trying to master and (inverse) design battery interfaces must combine the characterization of these interfaces in time as well as in space (spatio-temporal characterization) with physical and data-driven models integrating dynamic events at multiple scales, e.g. from the atomic scale to the micron scale. Therefore, BIG aims at establishing the fundamental “genomic” knowledge of battery interfaces and interphases through time, space and chemistries.
The Battery Interface Genome is related to the concept of descriptors in catalyst design, where the binding energy of other important reaction intermediates scales with that of the descriptor, and the identification and quantification of the descriptor value enables an accelerated and accurate prediction of the rate of the total reaction. Identifying the multitude of descriptors (or genes) coding for the spatio-temporal evolution of battery interfaces and interphases is a prerequisite for inverse design process, and simply cannot be established within existing methodologies. This requires the improvement of the capabilities of multi-scale modelling, artificial intelligence and systematic multi-technique characterization of battery interfaces, including operando characterization, to generate/collect comprehensive sets of high fidelity data that will feed a common AI-orchestrated data-infrastructure in the MAP.
To accelerate the discovery and (inverse) design of battery interfaces and interphases, a fully automated platform in which BIG, autonomous material synthesis and characterization and battery cell assembly will be integrated must be developed. While the traditional paradigm of trial-and-error based sequential materials optimization starts from a known interface composition and structure, and subsequently relies on human intuition to guide the optimization to improve the performance, the forward vision is to enable inverse materials/interface design, where one effectively inverts this process by allowing the desired performance goals to define the composition and structure which best fulfils these targets without a priori defining the starting composition or structure of the interface.
In short term: Establish formats and standards of a shared BIG-MAP data infrastructure for closed loop materials discovery; Autonomous analysis modules for experiments and simulations results using AI; Computational workflows to identify and pass features between scales; Data-driven materials and interface models guided by physical understanding.
In medium term: Implementation of the autonomous BIG-MAP platform capable of integrating computational modelling, autonomous synthesis robotics and materials characterization; Demonstrate inverse design of battery materials and interphases; Integration of sensing and self-healing in BIG-MAP.
In long term: Fully autonomous and chemistry neutral BIG-MAP platform establish and demonstrated; Integration of battery cell assembly and device-level testing; Inclusion of manufacturability and recyclability in the materials discovery process; Digital twin for ultra-high throughput testing on cell level implemented and validated