Complexity in Functional Materials


Prof. Mikko Alava


Orcid 0000-0001-9249-5079Orcid 0000-0001-9249-5079
Professor Mikko Alava is a world-class expert in the physical properties of materials and their dependence on structure, including transport properties. He has worked extensively on materials science data analytics and applications of modern machine learning approaches. Mikko Alava holds a PhD in nuclear engineering (Helsinki University of Technology) in fusion plasma physics, from 1993. He is since 2009 a full professor of physics at Aalto University, Finland. Mikko Alava has worked after a research direction change from fusion to materials on statistical physics applications to the physics of materials and on challenging computational problems in understanding fracture, friction, plasticity and other complex properties, typical of functional materials and their dependence on structure – defects, surfaces and so on. Lately, for 2012-17 he has been in Finland a vice director of a national Center of Excellence in Computational Nanoscience (COMP) and he has extensive international science manage experience eg. from the European CECAM organization and others. The scientific achievements of the Director of the NOMATEN include over 250 scientific papers including 40 in first rate journals such as Science Advances, Nature Communications, PNAS, and Physical Review Letters.


Background of the group is in fracture, plasticity, statistical mechanics of materials and the structure-property relationship. The group has expertise in machine learning and multiscale models and connections to European High Performance Computing Centre JU and Centre Européen de Calcul Atomique et Moléculaire, CECAM. Plans include plasticity of complex alloys (HEA, PLC effect), and collaborations with the other groups (plasticity, Machine Learning, multiscale models, mechanical properties across scales, corrosion using eg. phase field techniques). The RG may have a potential contribution to NDA/experimental analysis (DIC, Digital Image Correlation) using also Machine Learning methods to classify data and predict.