The functionality of natural and human-made materials depends on atomic-scale processes, which molecular engineering can control to fine-tune properties, enabling materials to adapt reversibly and precisely to external stimuli and their environment. Located at the interface of chemistry, physics, and materials science, our research group aims to address fundamental challenges in this field by developing novel concepts to encode information at the atomic scale, decode it on-demand in response to external inputs, and translate it into targeted functionality.

To this end, our group develops advanced computational methods to bridge different time and length scales, which are crucial for a comprehensive understanding of hierarchical nanomaterials. By combining theoretical chemistry with machine learning and evolutionary algorithms, we conduct multi-scale investigations to predict, rationalize, and screen responsive and adaptive functional materials.

Supported by the Heisenberg Program of the German Research Foundation, we have recently moved to the Interdisciplinary Center for Scientific Computing at Heidelberg University, where we continue advancing our mission to develop innovative concepts for intelligent, cell-like materials. These materials hold transformative potential for applications in catalysis, sensing/photonics, energy and information storage, soft robotics, separation and purification, and drug delivery.

If you are interested in our work, please feel free to contact us!

NEWS

We have moved to the Heidelberg University!

Our webpage is currently being gradually updated.

Thank you for your patience!

Multiple PhD positions available

in our group 

at the Heidelberg University

on various topics in the field of

computational and theoretical chemistry

including

• Multiscale simulations of stimuli-responsive functional materials
• Investigation of structural and dynamic properties of molecular switches and machines
• Computational Surface Chemistry/Physics
• Atomistic investigations of porous materials (e.g. MOF, COFs, cages)
• Development and application of evolutionary algorithms, neural networks and machine learning for modeling, evaluation and visualization of dynamic phenomena

Please apply:

phdheidelberg@gmail.com

Prof. Dr. Saeed Amirjalayer 

Interdisciplinary Center for Scientific Computing

University of Heidelberg

Stimuli-Responsive Nanomaterials Group

Email: saeed.amirjalayer@iwr.uni-heidelberg.de