A new €30 million research initiative called ASCEND is combining artificial intelligence with automated laboratories to dramatically speed up the discovery of catalysts that could help the chemical industry abandon coal and oil. The five-year project, launching April 1, 2026, brings together six partners including BASF, Siemens Energy, and leading research institutions to tackle one of the biggest bottlenecks in the green energy transition: finding materials that work at industrial scale .

What Are Self-Driving Laboratories and How Do They Speed Up Discovery?

At the heart of ASCEND is a concept that sounds like science fiction but is very much real: self-driving laboratories, or SDLs. These are automated research facilities where AI systems design experiments, robots carry them out, and the results feed back into the AI to design the next round of tests. Unlike traditional labs where scientists manually run experiments one at a time, SDLs operate in continuous learning loops, exploring vast material spaces that would take human researchers years to investigate .

The AI doesn't work alone. It builds digital twins, which are virtual models of the chemical systems being studied, and uses these models to decide which experiments to run next. Each result improves the model, making the next experiment more likely to succeed. This iterative approach is what makes the discovery process so much faster than conventional methods.

"The AI-driven approach of ASCEND allows us to explore vast material spaces that were previously inaccessible," said Karsten Reuter, project leader at the Fritz-Haber-Institute of the Max Planck Society.

Karsten Reuter, Project Leader at Fritz-Haber-Institute

The key insight is that humans remain essential. Scientists still define the research questions and guide the overall strategy. The AI simply handles the tedious work of planning and executing experiments, freeing researchers to focus on interpretation and breakthrough thinking .

How Does This Technology Translate to Real Industrial Applications?

ASCEND's goal isn't just faster discovery for its own sake. The project specifically targets catalysts needed for two critical industrial processes: green hydrogen production and sustainable chemical manufacturing. These are drop-in substitutes, meaning they can replace existing materials without requiring factories to completely retool their operations .

The project combines three technological approaches to ensure results are industrially trustworthy:

• Digital Catalysis: AI and simulations accelerate the discovery of high-performance materials by exploring chemical space far faster than traditional methods.
• Thin-Film Catalysts: These reduce the amount of material needed while improving efficiency, lowering both costs and environmental impact.
• 3D Structures: Layered catalyst designs enhance surface area and reaction control, making the catalysts more effective at scale.

The partnership includes BASF, one of the world's largest chemical manufacturers, which brings real-world manufacturing expertise. This isn't a purely academic exercise. BASF needs to validate new catalyst materials early in development to move promising research into actual production .

"This project allows us to validate new catalyst materials at an early stage, which is critical for moving promising research into technological application," explained Wolfram Stichert, Senior Vice President at BASF SE.

Wolfram Stichert, Senior Vice President at BASF SE

Why Does Catalyst Discovery Matter for Defossilization?

The chemical industry is one of the most energy-intensive sectors on the planet. It relies heavily on fossil fuels not just for energy, but as raw materials for producing everything from plastics to pharmaceuticals. Catalysts are the molecular machines that make chemical reactions possible. Better catalysts mean reactions happen faster, at lower temperatures, and with less waste .

For the industry to become independent of coal and oil, it needs catalysts that can efficiently convert renewable feedstocks like biomass and captured carbon into useful chemicals. Finding these catalysts through traditional trial-and-error methods could take decades. ASCEND's AI-driven approach could compress that timeline significantly, making the green transition economically viable for manufacturers.

The €30 million funding from Germany's Federal Ministry for Science, Technology and Space reflects the strategic importance of this work. The project targets the defossilization of energy-intensive industries while safeguarding industrial competitiveness, a balance that's crucial for getting manufacturers to actually adopt green technologies .

Steps to Understanding How AI Accelerates Scientific Discovery

• Automated Experimentation: Robots and automated systems run experiments 24/7 without human intervention, compressing months of work into weeks.
• Continuous Learning: Each experiment result updates the AI's digital model, making subsequent experiments more targeted and efficient.
• Parallel Exploration: Instead of testing one material at a time, AI can design multiple experiments simultaneously, exploring different chemical pathways in parallel.
• Human-AI Collaboration: Scientists interpret results and guide the research direction, while AI handles the computational heavy lifting and experimental design.

The ASCEND project represents a broader shift in how scientific research works. Rather than replacing human expertise, AI augments it, allowing researchers to ask bigger questions and explore possibilities that would be impractical to investigate manually. For an industry facing pressure to decarbonize while remaining competitive, that acceleration could be transformative .