Nong Artrith's research team is part of Utrecht University’s Pathways to Sustainability (PtS - Energy in Transition) initiative, addressing critical challenges in sustainable energy. Her research applies advanced computational methods to study and design complex materials, with a focus on amorphous and nanostructured systems. In addition to exploring these materials, Nong’s group develops innovative computational tools and methods, which are shared openly to benefit the scientific community. Students in the group will engage in groundbreaking work at the intersection of artificial intelligence (AI), machine learning (ML) quantum simulations, and materials science, contributing to real-world solutions for sustainability.
If you are driven by a passion for sustainability and computational science, join Nong’s team to help shape a greener future!
More about research ... and team.
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
2021-Artrith-Research-Overview.pdf