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Data NanoAnalytics

The Data NanoAnalytics Group’s mission is “To accelerate the development of autonomous research tools and workflows capable of scientific discovery in nanoscale synthesis and characterization by combining simulations, physics-driven machine learning methods and instrument automation.” 

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Our group is known for developing and implementing physics-driven machine learning experimental workflows to increase the capabilities of instruments for both nanoscale characterization and synthesis. To this end, we develop and apply machine learning tools to analyze multi-modal data streams, including imaging data from microscopy, hyperspectral data (e.g., STEM-EELS) and other forms of spectroscopy.

We utilize these algorithms, alongside human-in-the-loop knowledge injection, automation, simulations, and edge computing, to steer autonomous instruments and assist in materials modification, optimization, and ultimately, in discovering new physics. We are well-recognized for our physics-driven approach to autonomous science, through awards (e.g., R&D100 Award 2023), granted patents, and a history of publications and invitations to lecture on this topic.

We use these new capabilities to explore the frontiers of nanoscience: to study and perturb interfaces in ferroelectric and ferroelastic materials, explore structural phase transitions as a function of electrical stress and temperature, manipulate topological structures, rearrange individual atoms deterministically to form structures with designer properties with a microscope, and optimize growth of chalcogenides to enhance opto-electronic properties, amongst others. 


Group Leader, Data NanoAnalytics Group at the CNMS
Rama Vasudevan