Nongnuch Artrith

Research Scientist

Department of Chemical Engineering, Columbia University


Nong Artrith is a Research Scientist in the Department of Chemical Engineering at Columbia University and is also funded by the Center for Functional Nanomaterials at Brookhaven National Lab. Nong is also part of the Columbia Center for Computational Electrochemistry (https://ccce.chem.columbia.edu)

Nong obtained her PhD in Theoretical Chemistry from Ruhr University Bochum, Germany (Prof. Jörg Behler) for the development of machine learning models for applications in chemistry and materials science. She was awarded a fellowship from Schlumberger Foundation (supporting women in STEM) for postdoctoral work at MIT with Prof. Alexie M. Kolpak, where she applied machine learning methods to understand catalyst systems. She subsequently joined Prof. Gerbrand Ceder’s group at UC Berkeley to apply machine learning models to the understanding of amorphous electrode materials for Li-ion batteries. In 2019, she has been named a Scialog Fellow for Advanced Energy Storage.

Nong is the main developer of the atomic energy network (ænet) (http://ann.atomistic.net), a package for the construction and application of machine learning potentials.

Research Interests

Nong applies computational methods to understand and design realistic complex materials. A specific research focus is on amorphous and nanostructured materials. To enable the modeling of such complex systems, she develops novel methods and tools that are shared with the scientific community. More about research…

Materials for energy applications

  • Catalysis for carbon capture and for the production and use of synthetic fuels
    E.g., hydrogen, methanol, or ethanol production; CO2 conversion
  • Electrochemical energy storage

Materials for clean water applications

  • Materials for water desalination by nanofiltration or electrodialysis
  • Nanoporous materials for water filtration and purification

Development of machine learning methods and tools for materials science

  • Machine learning approaches for accelerated first-principles calculations
  • Machine learning methods for large-scale atomistic simulations
  • Public open-source frameworks/tools
N. Artrith* J. Phys. Energy 1, (2019) 032002. Open Access.