Nong Artrith

Expertise: Computational & theoretical chemistry to model and predict the surface composition of catalysts

As a computational/theoretical materials chemist, I am looking forward to engaging with the members of the ARC-CBBC consortium and its industry partners. I aim to take my research from fundamental science to practical industrial applications. The consortium provides a unique platform for learning from one another and working together on significant scientific challenges. As a member, my goal is to form lasting partnerships, particularly with industry collaborators, and to use my expertise in machine learning and atomic-scale simulations to tackle the most pressing scientific issues facing the chemical industry.

About my research

My research focus is on atomic-scale computational materials chemistry combining electronic-structure methods (e.g., density functional theory) and machine learning (ML) for the computational understanding and discovery of materials and for the interpretation of experimental observations. My group is developing the free and open-source ML package ænet (http://ann.atomistic.net) for ML-accelerated atomistic simulations and materials property predictions. With our techniques, we can model complex surfaces and nanoparticles that are otherwise challenging to characterise.

Academic career / Personal website: http://nartrith.atomistic.net