Maciej Haranczyk

Computational and Data-Driven Materials Discovery

Dr. Maciej Haranczyk obtained a PhD degree in Chemistry from University of Gdansk in Poland in 2008. During his graduate studies, he received numerous research fellowships to collaborate with researchers at Pacific Northwest National Laboratory, University of Southern California, University of Sheffield, and others. After his graduate school, he moved to Lawrence Berkeley National Laboratory, which had offered him a prestigious Glenn T. Seaborg Fellowship. He was then hired into a tenured Research Scientist position (2010) and subsequently promoted to a Staff Scientist position (2014).

He joined IMDEA Materials Institute as a Senior Researcher in 2015, and was supported by a Ramón y Cajal award.

Dr. Haranczyk co-authored 149 research publications in leading journals. His work has been highlighted in numerous news articles as well as on covers of top-notch journals such as Science, Energy&Environmental Science and others. Regarding project experience, Dr. Haranczyk participated in 21 projects funded by different regional (Regional Government of Madrid), national (Spanish Ministry of Science and Innovations, The US Department of Energy) and international (European Commission) public entities as well as by the Industry.

maciej.haranczyk@imdea.org

Publications at IMDEA Materials

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  • RESEARCH INTERESTS
  • ACADEMIC CAREER
  • HONORS AND AWARDS

His research interests are focused on the development and applications of novel methodology aiding material discovery.  His work effectively combines materials informatics approaches with traditional computational material science techniques as well as data-driven experimentation. In the broad spectrum of his on-going activities, the following long-lasting threads are clearly identified:

  • Feature engineering and material structure representations for machine learning.
  • Enumerated and/or curated datasets of nanoporous materials structures.
  • Development and applications of similarity-searching-, machine-learning- or optimization-based material discovery approaches in the context of porous materials for storage, separations and catalysis.
  • Data-driven experimentation involving machine learning-based surrogates of real experiments (digital twins), and employed in, for example, the design of nanoporous clays and their composites for applications in catalysis, rheology and detoxification of animal feeds as well as composite materials involving porous particles.

Furthermore, two emerging areas are currently actively investigated:

  • Development of materials genome approaches aiding the design of 3D printed porous metal structures.
  • Autonomous laboratory setups involving collaborative robots.
  • Since 2015 Head of the Computational and Data-Driven Materials Discovery group at the IMDEA Materials Institute, Spain.
  • 2014 Staff Scientist, Lawrence Berkeley National Laboratory, USA
  • 2010 Research Scientist, Lawrence Berkeley National Laboratory, USA
  • 2008 Glenn T. Seaborg Postdoctoral Fellow, Lawrence Berkeley National Laboratory, USA
  • 2008 PhD in Chemistry, University of Gdansk, Poland.
  • 2016 R&D 100 Award for the Carbon Capture Simulation Initiative (CCSI) project
  • 2015-2020 “Ramón y Cajal Fellowship from the Spanish Ministry of Economy”
  • 2008 Glenn T. Seaborg Postdoctoral Fellow, Lawrence Berkeley National Laboratory, USA