TeraBatt
Data-driven quest for TWh-scalable Na-ion battery.
About the project
The Data-driven quest for TWh-scalable Na-ion battery (TeraBatt) project aims to revolutionise the discovery of sustainable sodium-ion battery cathode materials through multi-objective, generative inverse design.
Sodium-ion batteries are a promising alternative to lithium-ion technology for large-scale energy storage but currently lack cathode materials that combine cost-effectiveness, earth-abundant compositions, high electronic conductivity, and long cycle life.
TeraBatt introduces a fundamentally new paradigm for materials innovation by enabling exploration of an effectively infinite chemical and structural phase space using physics-informed deep learning models trained on thousands of high-accuracy quantum-mechanical simulations. This "machine that builds the machine" approach is designed to accelerate the discovery of synthesizable, high-performance polyanionic cathodes suited for TWh-scale grid storage.
Within TeraBatt, DTU develops an integrated framework that couples advanced graph-based neural network potentials, conditional generative models, and automated high-throughput DFT workflows to predict total energies, electronic transport properties, and realistic synthesis pathways for millions of candidate materials.
Selected compositions will be synthesized and electrochemically tested, with in-situ high-temperature diffraction used to validate predicted formation mechanisms. The goal is to deliver a cobalt-free sodium-ion cathode that retains at least 80% of its capacity after 5000 cycles, outperforming current state-of-the-art materials and enabling cost-efficient and scalable production without nanosizing or other expensive processing steps.
By uniting AI-driven inverse design with experimental validation, TeraBatt aims to redefine how functional battery materials are discovered and accelerate the green-energy transition.
Type of project
DFF
Section members involved in the project
Contact: Juan Maria Gracia Lastra
Juan Maria García Lastra Professor, Head of Section Department of Energy Conversion and Storage Mobile: +45 93511632 jmgla@dtu.dk
Arghya Bhowmik Associate Professor Department of Energy Conversion and Storage Mobile: +45 31844747 arbh@dtu.dk
Martin Hoffmann Petersen Student Students s230520@student.dtu.dk
François Raymond J Cornet PhD student Department of Applied Mathematics and Computer Science frjc@dtu.dk
Poul Ægidius Norby Professor Department of Energy Conversion and Storage Mobile: +45 21124450 pnor@dtu.dk