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Pulsenics proudly announces our membership in ATLAS.LIB, a consortium within the Canada-Germany 3+2 Collaborative Industrial Research and Development Program. This bilateral government-supported initiative is designed to foster joint R&D partnerships between Canadian and German companies. The program, managed by the National Research Council of Canada (NRC) and the German Federal Ministry for Education and Research (BMBF), leverages the incredible research capabilities of self-driving labs to accelerate electrification worldwide.
Self-driving labs use electrochemical impedance spectroscopy (EIS) from Pulsenics to non-invasively scan batteries that contain experimental battery recipes. AI models use that data to predict lifetime performance, select the most promising formula, and direct robotic synthesis of the next novel chemistries. It is conceptually similar to the way that medical researchers use AI to develop proteins, molecules, or vaccines.
ATLAS.LIB has set itself the goal of significantly improving the performance and service life of lithium-ion batteries (LIBs), which are primarily used in electric vehicles. The focus is on the targeted optimisation of the interfaces between electrodes and electrolyte, which are crucial for the stability, fast-charging capability and energy density of the cells.
Pulsenics will support groundbreaking research on both sides of the Atlantic by incorporating EIS workflows into battery testing. Our spectroscopy data provides deeper insights, faster, than traditional electrical tests. Pulsenics has been honored to collaborate with experts across multiple institutions, including NRC Mississauga, McMaster University, Preli GMbH, and Helmholtz Institute Münster (HI MS) of Forschungszentrum Jülich.
The project is pursuing an innovative approach: automated high-throughput experiments (HTE), the use of artificial intelligence (AI) and machine learning (ML) are being used to systematically test and evaluate chemical compositions and material combinations. The aim is to uncover synergy effects between electrodes and electrolyte that enable stable battery performance even at high voltages.
In Canada, Pulsenics will support Canada-based research focus on high-power battery cathodes using Canadian-mined nickel. Here’s how NRC described the project in a recent press release:
Pulsenics’ EIS technology should empower the NRC’s CBMI Self-Driving Lab to conduct in-line characterization of key battery properties for numerous new cathode formulations at a pace that matches BattMAP’s existing high-throughput synthesis. By eliminating characterization as a bottleneck, this integration should enable a seamless, end-to-end workflow – from synthesis to characterization – making a significant leap from the previous approach of testing each formulation individually and unlocking true high-throughput materials discovery.
In Germany, Pulsenics will support the Helmholtz Institute Münster (HI MS) of Forschungszentrum Jülich. German-based research will focus on battery electrolytes. Here is how the Helmholtz Institute described the project in a recent blog post:
ATLAS.LIB has set itself the goal of significantly improving the performance and service life of lithium-ion batteries (LIBs), which are primarily used in electric vehicles. The focus is on the targeted optimisation of the interfaces between electrodes and electrolyte, which are crucial for the stability, fast-charging capability and energy density of the cells.
The project is pursuing an innovative approach: automated high-throughput experiments (HTE), the use of artificial intelligence (AI) and machine learning (ML) are being used to systematically test and evaluate chemical compositions and material combinations. The aim is to uncover synergy effects between electrodes and electrolyte that enable stable battery performance even at high voltages.
The interplay of automated experiments, data-driven analysis and machine learning marks a paradigm shift in battery material research. Instead of empirical individual tests, material combinations can be investigated on a large scale and optimised using learning algorithms. This allows new, industrially compatible electrolyte formulations to be identified and validated more quickly.
Results from the self-driving labs will result in full cell assembly with down-selected materials. Pulsenics will perform rapid cell-level tests and unify the data estate across the full battery assembly, thereby de-risking the development workflow for new battery chemistries.