Scroll to Section:

On an atomic scale, the area of a material in which different crystalline structures come together is known as a grain boundary. In this video, LIAM HUBER investigates the behavior of alloying elements at grain boundaries. Identifying impracticalities in quantum mechanical simulation, Huber uses classical simulation to study how atoms behave at the grain boundary and how this influences the properties of that grain boundary. Huber also employs machine learning to make predictions and to extend the insights provided by the study. Going forward the research will seek to further explain the influence of a range of variations including, most significantly, temperature.


Liam Huber is Group Leader for Thermodynamics and Kinetics of Defects at the Max-Planck-Institut für Eisenforschung in Düsseldorf. Huber has been working in the Computational Materials Department at the Max-Planck-Institut since 2015. Prior to this, he completed his Masters and PhD in Physics at the University of British Columbia. Huber’s research focuses on solute defect interactions, computational methods for long timescale problems, machine learning in materials science and pyiron development. An expert on the computing language Python, Huber received awards from the Canadian NSERC for both his doctoral and masters research projects.


Max-Planck-Institut für Eisenforschung

Novel alloys for automotive lightweight design and airplane turbines, materials for sustainable energy conversion and storage, and the development of big data and machine learning methods – these are just a few examples of the research areas that are being investigated by the scientists of the Max-Planck-Institut für Eisenforschung. The team of engineers, material scientists, physicists, and chemists develops tailored materials and methods for mobility, energy, infrastructure, and information. To this end, the researchers study complex materials with atomic precision under real environmental conditions.
Show more

Original publication

A Machine Learning Approach to Model Solute Grain Boundary Segregation

Huber Liam, Hadian Raheleh, Grabowski Blazej and Neugebauer Jörg
npj Computational Materials
Published in 2018

Reading recommendations

The Spectrum of Binding Energies Approach to Grain Boundary Segregation

White C.L. and Coghlan W.A.
Metallurgical Transactions A
Published in 1977

Spectrum of Grain Boundary Segregation Energies in a Polycrystal

Wagih Malik and Schuh Christopher A.
Acta Materialia
Published in 2019