Atomic Scale Materials Modelling (ASM)

About Us

The Atomic Scale Materials Modelling (ASM) section pioneers the computational design of advanced materials for clean energy conversion and storage. We explore how atoms and electrons behave in complex materials to uncover the principles that drive performance in batteries, optoelectronic devices, and catalytic systems for P2X technologies.

Our research blends density functional theory (DFT) with machine learning and data-driven modeling to accelerate materials discovery. Beyond using established tools, we actively develop new algorithms and computational workflows, pushing the limits of what atomic-scale modeling can achieve.

Our Focus

We aim to transform energy science through predictive modeling — from understanding catalytic reactions at the atomic level to discovering next-generation electrode and electrolyte materials for sustainable energy systems.

Key focus

Accelerating the discovery of next-generation energy materials through atomic-scale simulations and machine learning.

Recent Highlights

• Uncovered the mechanisms behind novel catalysts for ammonia synthesis
• Advanced understanding of redox processes in post-lithium-ion batteries
• Uncovered how triplet–singlet state inversion enhances OLED performance
• Developed accelerated methods for ionic transport simulations

Collaboration and Impact

ASM works closely with world-leading academic and industrial partners, including Stanford University, MIT, University of Cambridge, KIT, CEA, Osaka University, and DICP, enabling cross-disciplinary research and innovation.

Our collaborations span Europe, the USA, Asia, Africa, and South America, fostering joint projects, co-publications, and researcher exchanges that strengthen the global energy research community.