Meet people at WISE: Andreas Larsson

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Andreas Larsson is a Chair Professor of Applied Physics in the Division of Materials Science within the Department of Engineering Sciences and Mathematics at Luleå University of Technology (LTU). He earned his Ph.D. in Theoretical Chemistry from Göteborg University and subsequently undertook two postdoctoral research positions: first as a Marie Curie Fellow at the National Microelectronics Research Centre (NMRC) at University College Cork, and later with the Condensed Matter Theory Group at Uppsala University.

Prof. Larsson leads the Applied Physics group at LTU, which focuses on investigating the properties of materials and their interfaces. The group’s research integrates electronic structure calculations, atomistic modeling, and machine learning to deepen the understanding of material behavior and properties.

What is your role in WISE and why did you choose to join it?

I am the newly appointed co-Director of the WISE Research School, where I work alongside Professor Anna Delin. I chose to join WISE because I am passionate about sustainable development in materials science and wanted to help nurture the next generation of researchers in this field. My role involves supporting the development of educational programs such as courses, summer schools, study trips, and a mentoring program to ensure the success of WISE.

How does the WISE Research School contribute to reach the goals of WISE?

The WISE Research School plays a crucial role in helping doctoral students and postdoctoral researchers contribute to the green transition through materials research. By providing a strong foundation in sustainable development and offering courses on relevant materials issues, we ensure that sustainability is a key focus in all our activities.

As a researcher yourself, what materials science challenge is closer to your heart?

As a researcher, my work uses electronic and atomistic modeling of materials to enable more efficient electronics and energy storage. I am particularly interested in developing methods for modeling materials growth to better understand and control the production of nanomaterials with consistent properties. This requires the use of machine learning force fields trained on quantum mechanical simulations of the materials and their interactions. This work is essential for harnessing the full potential of nanomaterials in technology products with reduced energy consumption and improved batteries.