AI-driven Catalyst Discovery: €30 million funding for German consortium
ASCEND Consortium: Helmholtz-Zentrum Berlin, Fritz-Haber-Institut der Max-Planck-Gesellschaft, BASF, Dunia Innovations, Siemens Energy, Technische Universität Berlin / BasCat
A robotic arm precisely moves an empty sample container back and forth between automated workstations. © Dunia Innovations
The ASCEND team at the intern kick-off meeting. © FHI / Steffen Kangowski
Six partners from research and industry, including Helmholtz-Zentrum Berlin (HZB), the Fritz-Haber-Institute of the Max Planck Society (FHI), BASF, Dunia Innovations, Siemens Energy, and the Technical University Berlin are launching a joint project to accelerate the catalyst discovery. The German Federal Ministry for Science, Technology and Space (BMFTR) is providing €30 million in funding for ASCEND (Accelerated Solutions for Catalysis using Emerging Nanotechnology and Digital Innovation). The research initiative targets the defossilisation of energy-intensive industries while safeguarding industrial competitiveness, with a focus on the chemical sector. The five-year project will start on 1st April 2026.
ASCEND aims to accelerate the development of next-generation catalysts, a cornerstone of sustainable chemical manufacturing, by bringing together two breakthrough approaches: Digital Catalysis and thin-film catalyst technologies. Digital Catalysis uses Artificial Intelligence (AI), simulations, and self-driving laboratories (SDLs) to speed up the discovery of high-performance materials, while thin-film catalysts reduce material use and improve efficiency. Combined with 3D structures, these catalysts enhance surface area and reaction control, towards the goal of providing sustainable syn-fuels and base chemicals as drop-in substitutes in industry.
AI-accelerated material discovery
At the heart of ASCEND’s approach is AI, which, together with automation and robotics, powers SDLs to accelerate scientific experiments. The AI autonomously builds and updates digital twins of the systems under study and bases its design decisions on these models. It designs experiments that are carried out by automated systems in iterative learning loops, using each result to improve the next step. While AI significantly speeds up planning and efficiency, humans remain essential for guiding the overall approach and defining the scientific questions.
The project builds on the long-term successful partnership between the Fritz Haber Institute and Helmholtz-Zentrum Berlin to drive forward catalysis research.
The scientific project coordinators emphasise the strategic importance of the funding: "The AI-driven approach of ASCEND allows us to explore vast material spaces that were previously inaccessible” says project leader Karsten Reuter, FHI. His co-project lead Michelle Browne, HZB adds "It fundamentally changes how fast the science can deliver the solutions chemical industry urgently needs."
The goal is not autonomous experimentation for its own sake, but industrially trustworthy results. In ASCEND, by combining AI with physical synthesis and stress testing under manufacturing-relevant conditions, Dunia accelerates learning while maintaining confidence at scale,” says Marcus Tze-Kiat Ng, Chief Technology Officer of Dunia Innovations.
From research to industrial application: Strengthening technology leadership
By accelerating catalyst development cycles, ASCEND aims to unlock the performance breakthroughs needed for commercially viable, large-scale deployment of green hydrogen and sustainable chemicals. This is an important prerequisite for the industry to become independent of coal and oil products.
"This project allows us to validate new catalyst materials at an early stage, which is critical for moving promising research into technological application," says Wolfram Stichert, Senior Vice President at BASF SE.
Background
According to S&P Global Ratings, the chemical industry is accountable for approximately six per cent of global greenhouse gas emissions. This corresponds to the annual emissions of the European Union (according to EDGAR – Emissions Database for Global Atmospheric Research). These emissions are primarily generated during electricity production in power stations, where fossil fuels are burned. They are also produced during the manufacture of plastics, fertilisers and pharmaceuticals, which are mainly derived from fossil fuels.
Catalysts can provide a solution. According to estimates, around eighty per cent of all chemical products undergo a catalytic stage in their manufacturing process. Innovations in the field of catalysts are therefore crucial for industrial transformation with the aim of reaching greenhouse gas-neutral production by 2050.
- Nanosilver as an electrocatalyst for CO₂ reductionVia electrolysis, CO2 can be reduced to CO, a raw material for further chemical products such as fuels. Within the GreenQuest Project, an internation team led by HZB chemist Prashanth Menezes has now systematically investigated catalyst layers made of silver nanoparticles, varying both the size of the particles and their density. The best yield was achieved with nanoparticles with diameters of around 10 nm, which were loosely distributed. Furthermore, they demonstrated how the economic efficiency of the electrochemical cell can be enhanced by integrating an additional chemical reaction at the anode, enabling the simultaneous production of a valuable formic acid, hydrogen, and CO in one device.
- Spin waves inside a nano-oscillator imaged for the first timeFor the first time, researchers have directly imaged the magnetisation dynamics inside a spin Hall nano-oscillator — a nanoscale device that converts direct current into tunable microwave signals and is a promising building block for energy-efficient wireless communication and brain-inspired computing. A Swedish–German team led by the University of Gothenburg and Helmholtz-Zentrum Berlin (HZB) achieved this using time-resolved scanning transmission X-ray microscopy at the MAXYMUS instrument at BESSY II. The results, now published in Advanced Materials, reveal spin-wave features that had escaped previous, indirect measurement techniques.
- Joint power instead of duplicate structures:Berlin’s research community is further advancing its research excellence by establishing a high-performance, cross-institutional infrastructure for data and AI. With a joint agreement signed on 18 September 2026, the Berlin University Alliance (BUA), the Helmholtz-Zentrum Berlin (HZB) and the Zuse Institute Berlin (ZIB) are paving the way for a joint data science and AI centre in Berlin-Dahlem and Adlershof.