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Department Artificial Intelligence for Integrated Material Science
Research Group

Intelligent Materials and Computational Catalysis

The Intelligent Materials and Computational Catalysis group focuses on the design and discovery of functional materials for sustainable catalysis by integrating atomistic simulations, data-driven approaches, and artificial intelligence.

Dr. Xiangyu Guo

Research Group Leader

Room: 4-137
Tel.:
E-Mail: xiangyu.guo@rub.de

Research

Our goal is to understand and control the electronic structure, interfacial stability, and catalytic mechanisms of advanced materials under realistic electrochemical and photochemical conditions. Key research areas include:

Electrocatalyst and photocatalyst design: Development of descriptors, stability models, and multi-center design principles for next-generation high-performance catalysts.
Material-integrated field-effect catalysis: Exploiting intrinsic physical properties of materials to emulate the influence of external fields and enable dynamic pathway control.
High-throughput and data-driven discovery: Building open-access databases and predictive models to accelerate the exploration of complex material systems.
Reaction network exploration: Creating automated tools to map reaction networks, identify mechanisms, and optimize selectivity toward targeted products, with emphasis on C–C coupling and organic catalytic transformations.

Schematic illustration of intelligent digital platform for catalysts discovery.

ICAMS, RUB

The ultimate aim is to establish intelligent digital platforms for materials discovery, advancing both fundamental understanding and practical applications for carbon-neutral technologies and green molecular manufacturing.

Competences

High-throughput density functional theory calculations
Phase diagram and surface structure analysis
Adaptive strategies for catalyst design
Statistical analysis and machine learning for materials prediction

Members

Recent Publications

Research Examples

Data-driven pursuit of electrochemically stable 2D materials with basal plane activity toward oxygen electrocatalysis

We developed a data-driven framework for discovering potential 2D materials with intrinsic basal plane activity and electrochemical stability toward oxygen electrocatalysis. Our computations unveiled the substantial effect of dissolution/oxidation on the stability of basal planes, especially in the OER process, which explains well the in situ formation of oxides or hydroxides for some oxygen electrocatalysts.

Transformation of CO₂ to C₂₊ alcohols by tailoring the oxygen bonding via Fe-based tandem catalyst

We developed an iron-based tandem catalyst that effectively addresses the long-standing activity–stability trade-off in CO₂ hydrogenation, achieving record-breaking performance with 49.1% selectivity toward C₂₊ alcohols and stable operation for over 1,000 hours.