Hi👋 I am

Christopher

Christopher Kue

Christopher Kuenneth,

a Alexander von Humboldt postdoctoral fellow in materials informatics at Georgia Tech.

About Me

  • Name: Christopher Künneth (Kuenneth)
  • People call me: Chris, Kü
  • Date of birth: April, 20th 1988
  • Pronouns: He/him/his
  • Nationality: German
  • Email: christopher.kuenneth@gmail.com
  • Affiliation: Georgia Institute of Technology

Who I am ?

Hi 👋 I am Chris, and I am an Alexander von Humboldt postdoctoral fellow in materials informatics at the Georgia Institute of Technology in Atlanta.

What I Did ?

I recieved my PhD (Latin: Dr. rer. nat. [doctor rerum naturalium], German: Doktor der Naturwissenschaften) from the Technical University Munich in collaboration with the Munich University of Applied Sciences in 2018.

During my doctorate, I investigated the ferroelectric and pyroelectric effects in hafnium dioxid (HfO\(_2\)) and zirconium dioxid (ZrO\(_2\)) thin films (thickness ~\(\text{nm}\) range) using materials informatics tools such as the density functional theory (DFT) or molecular dynamics (MD). HfO\(_2\) and ZrO\(_2\) are silcon-compatiable materials that are commonly used in electronic components (e.g., as gate dielectric of transistors). Their ferroelectricity was found by accident in 2011 when scientists at Qimonda (a German memory company at this time) conducted experiments on silicon doped HfO\(_2\) films. The discovery was a big surprise but also a tremendous opportunity because ferroelectricity in an inert and CMOS-compatible material like HfO\(_2\) opens many new application. The research question of my PhD was to identify the crystallographic phases that are responsible for the ferroelectricity and their interplay with external and internal physical factors (i.e., dopants, electric field, film thickness, etc.).

What I Do ?

After graduation, I was awarded the Feodor-Lynen fellowship for postdoctoral researchers by the Alexander von Humboldt Foundation, which I started in February 2019 in the Ramprasad Group at Georgia Institute of Technology in Atlanta. The Ramprasad group "develops and utilizes computational and data-driven tools to aid materials design" with a strong focus on polymeric materials.

Currently, my research focus lies on applying machine learning methods from the computer sciences community in materials sciences. I develop machine learning models, often based on neural networks, that predict and discover prospective polymers along with their manufacturing process for a variety of applications.

People may call me a "materials computer scientist".

Resume

Experience

  • 05/2021 - 07/2021

    Postdoctoral guest scientist

    Los Alamos National Laboratory (LANL), Los Alamos, USA
    Hosts: Ghanshyam Pilania, Blas Uberuaga

    Optimizing biodegradable polymers for packaging applications using machine learning.

  • 02/2019

    Feodor-Lynen postdoctoral fellow by the Alexander von Humboldt Foundation

    Georgia Institute of Technology (Georgia Tech), Atlanta, USA
    Advisor: Rampi Ramprasad

    I improve and develop polymeric materials using deep learning tools. Current position.

  • 06/2017 - 12/2017

    Research Scholar

    University of Connecticut (UConn), Storrs, USA
    Advisor: Rampi Ramprasad

    I stayed 6-month as a research fellow in the Ramprasad group at the University of Connecticut. I continued my research on HfO\(_2\) and ZrO\(_2\) and focused particularly on the ferroelectric switching kinetics and the role of grain boundaries. Outcomes were published in a Elsevier book chapter.

  • 09/2014 - 12/2018

    Graduate Researcher

    Technical University of Munich (TUM) & Munich University of Applied Sciences (MUAS), Munich, Germany
    Advisors: Alfred Kersch and Karsten Reuter

    In 2011, researchers unexpectedly discovered ferroelectricity and pyroelectricity in thin HfO\(_2\) and ZrO\(_2\) films. Aiming to understand these surprising discovery, I investigated HfO\(_2\) and ZrO\(_2\) using computational materials tools, helping experimentalists to further improvements their findings.

  • 03/2013 - 07/2014

    Student Research Assistant

    Munich University of Applied Sciences (MUAS), Munich, Germany
    Advisors: Alfred Kersch and Hans Christian Alt

    I was working on two projects sponsored by the German Research Foundation (DFG):

    • "Piezo spectroscopy and ab initio calculations of carbon oxygen complexes in gallium arsenide"
    • "INFEROX: Incipient ferroelectrics based on hafnium oxide"

Education

  • 2014-2018

    Dr. rer. nat. (Doktor der Naturwissenschaft, Ph.D.)

    Technical University of Munich (TUM), Munich, Germany.

    Latin honor: Summa Cum Laude (passed with highest distinction). Full 3-yrs. doctoral scholarship awarded by the German Excellence Initiative program.

  • 2011-2014

    M.Sc.

    Munich University of Applied Sciences (MUAS), Munich, Germany.

    Major: micro and nano technologies.

    Courses: semi-conductor physics, photonic, quantum mechanics, advanced quantum mechanics, physical simulation and modeling, micro and nano lab class, bio micro and nano technologies, micro and nano materials

  • 2008-2011

    B.Eng.

    Baden-Wuerttemberg Cooperative State University, Heidenheim, Germany.

    Major: mechanical engineering.

  • 2004-2007

    Allgemeine Hochschulreife
    (University entrance qualification)

    Robert-Bosch-Schule, Ulm, Germany.

    Majors: mathematic, physic and computer sciences.

Awards

  • 2019 - Dimitris N. Chorafas Prize for outstanding doctoral research
  • 2019 - Feodor Lynen research fellowship for postdoctoral researchers by the Alexander von Humboldt Foundation
  • 2019 - Summa cum laude (passed with highest distinction), Latin PhD honor
  • 2014 - Full doctoral scholarship awarded by the Technical University of Munich (German Excellence Initiative)

Projects

  • Agni Web A machine learning density functional theory emulation platform for atomistic calculations.
  • Polymer Entity Tagger (PET) RoBERTa based machine learning model that finds and annotates polymer names in text.
  • Co.PolymerGenome Copolymer property (Tg, Tm, & Td) predictor based on multitask neural networks.
  • RDKit on pypi pip install rdkit-pypi and you are done! No Conda.

Publications