Francesca Tavazza (Fed)

Francesca Tavazza has been a member of the scientific staff at the National Institute of Standards and Technology (NIST) since 2003. She earned an undergraduate degree in Physics and a MS in Materials Science from Universita’ Statale di Milano, Italy, and a PhD in Physics from the University of Georgia. She is currently leading the Data and AI-Driven Materials Science Group in the Materials Measurement Science Division, with focus on autonomous experimentation and machine learning (ML) applied to materials design and characterization. Before, she served as Project Leader of the “Electronic and Functional Materials” project, leading a team focused on computational modeling, high-throughput discovery, and AI/ML (ML = machine learning) investigation of solid-state material properties. Together with Kamal Choudhary (back then a postdoc of her), she started the JARVIS project (https://jarvis.nist.gov/), that now contains a large number of databases, web apps and tools, all available to the public. More than 45 papers have been published to this day (1/2025) associated with the project, with more than 3500 citations, and the data and/or tools have been downloaded more than 1 million times. 
Her areas of expertise include Density Functional Theory (DFT) modeling of quantum phenomena, electronic, optical and spectroscopic properties of standard and van der Waals-bonded materials, DFT and Molecular Dynamics investigation of mechanical deformations, uncertainty evaluation in DFT and ML, fitting of force fields and ML models, Monte Carlo simulations of atomistic behavior. She has published over a hundred papers in refereed journals and contributed to the organization of tens of workshops/symposia. She served as chair of the Computational Materials Science and Engineering committee, TMS, in 2020-2022, as well as is part of the TMS Materials Innovation Committee - subcommittee on AI. 

• 2019: MML Accolade award (NIST): MML Mentor