AI speeds up battery materials research – towards sustainable solutions

Artificial intelligence is a new addition to the traditional tool set in materials research. It can speed up the development of new battery materials and sustainable solutions. AI also provides an interesting opportunity for collaboration between research and industry.

In materials research, discoveries usually result from a time-consuming trial and error process. AI has the potential to change this. It can accelerate the development and optimization of new materials and reduce the need for human input. In the near future, we will even characterize new battery materials automatically. AI has also made us more proficient in generating data for materials research, which means that we can carry out more experiments in less time. Even if these experiments are simple, such AI-guided automation would deliver results faster.

Previously, materials research has had tools for understanding causalities, but lacked tools for design. This has been an obstacle for solving some typical problems, such as building different molecular architectures. With AI, we can address more complex problems and formulate them better. AI can handle large amounts of data and find correlations or patterns that help us understand the behavior of different materials. We can use this understanding to our advantage in battery design.

Furthermore, we can even bypass the detour via understanding and use AI directly for property prediction and optimization. If, for example, industry needs a better battery that lasts longer, AI can directly optimize the process and the materials without us knowing the individual details of how the materials behave. This is a big paradigm shift in materials research.

Focus on sustainability

Sustainability is one of the key issues in battery development. Batteries already play a crucial role in many sustainable solutions, such as energy storage or electric mobility. However, batteries themselves contain harmful materials.

AI can help us develop new, sustainable alternative materials. For example, current research explores new battery solutions, raw materials, or biobased materials. AI can aid in the characterization, design, and processing of these materials to decrease their time to market. We can also use AI to link materials research to the circular economy or to battery recycling to obtain materials from old batteries, or to optimize challenging extraction processes.

Next steps: collaboration to support research and industry

FCAI develops AI tools for many application areas, which are organized in FCAI highlights. AI for Materials Science is one of these highlights. Other recently launched highlights are AI in Atmospheric Science and AI for Sustainability. An AI for Batteries highlight could follow in these footsteps.

The timing is right for AI in battery research. Batteries are a hot topic in the search for sustainable solutions, and materials research is one of the focus areas. AI can help us raise the bar of our battery materials research on a national and an international level.

The battery domain is also a field where collaboration between research and industry can benefit both sides. With FCAI’s support, we can gain impact and develop a Finnish ecosystem for battery materials. There is already a high technology readiness level and plenty of funding for projects in this field, as well as on-going industry collaboration with direct impact on sustainability.

The Finnish industry is highly interested in this collaboration and know-how, as well as in increasing their own inhouse AI activities. A Finnish battery ecosystem could speed up the process and help us define where we can be competitive, come up with innovations and do research that supports our industry.

Anssi Laukkanen is research professor at VTT. Patrick Rinke is Associate Professor at Aalto University.