Prof. Patrick Rinke: making sustainable materials with AI

June 06, 2023

For nearly ten years, Professor Patrick Rinke of the Department of Applied Physics has been a pioneering presence in machine learning methodology in materials science. Since then, machine learning in materials science has become a global research trend. Ultimately, I did a physics degree while veering towards computational physics, which combined all three things I was good at,' Rinke says. Already in 2014—a decade before ChatGPT made AI ubiquitous—Rinke was curious about machine learning. ‘When I started as a professor, almost nobody in Finland was developing this kind of machine learning methodology in physics.

For nearly ten years, Professor Patrick Rinke of the Department of Applied Physics has been a pioneering presence in machine learning methodology in materials science. Rinke learned the ropes on the job, wrote algorithms and taught the methods already in 2016 when few had a clear idea what AI could do. 

Since then, machine learning in materials science has become a global research trend. Rinke’s expertise in finding sustainable and climate-friendly material configurations has arguably never been more in demand.

A curious start

Patrick Rinke came to Aalto in 2014 with a background in computational physics and quantum mechanics. For much of his academic career, from Germany to the UK to the US, he has modeled the complex behavior found at the atomic level with computational tools. 

'In high school, I was good at math, physics, and computer science. But I couldn't decide what I wanted to study at university. I thought math was too hard, and computer science too new. Ultimately, I did a physics degree while veering towards computational physics, which combined all three things I was good at,' Rinke says.

Already in 2014—a decade before ChatGPT made AI ubiquitous—Rinke was curious about machine learning. He found equally curious partners at Aalto, and together they experimented with AI and machine learning in a physics context.

‘When I started as a professor, almost nobody in Finland was developing this kind of machine learning methodology in physics. The people who hired me at Aalto were just as interested as I was as to whether it would work,’ Rinke says.

And work it did. Recently promoted to full professorship at Aalto, Rinke heads the Computational Electronic Structure and Theory (CEST) group who apply machine learning methodology of their own devising to advance materials science and help scientists from across disciplines to take their work to the next level.

The source of this news is from Aalto University

Popular in Research

Presidential Debate TV Review: Kamala Harris Baits Raging Donald Trump Into His Worst Self In Face-Off

Oct 21, 2024

Impact of social factors on suicide must be recognised

Oct 21, 2024

Print on demand business with Printseekers.com

Sep 6, 2022

The conduct of some Trump supporters is crude, sleazy and...deplorable

Oct 21, 2024

Students learn theater design through the power of play

Oct 21, 2024

MSN

Oct 21, 2024