Design and Fabrication of silicon microfluidics for combinatorial screening of catalytic reaction pathways for accelerated material discovery and chemical conversion.
Mathematics and algorithms for identifying configurations of complex physical systems exhibiting unique, anomalous properties.
An automated explainable bioinformatics and AI workflow for multi-omic, climate and environmental data, applied to sustainability problems e.g., nature-based carbon capture.
AutoML for incremental machine learning algorithms for big time-series data.
Leveraging our expertise in materials science, AI, quantum and high performance computing, we're developing a more powerful, sustainable, and energy-efficient battery.
We're addressing the environmental and human health impact of PFAS ‘forever chemicals’ by accelerating the discovery of sustainable replacements and improved capture materials.