Name: Maria Chan
Pronouns:
Biography:
Maria Chan is a scientist with the Center for Nanoscale Materials who studies nanomaterials and renewable energy materials, such as solar cells, batteries, catalysis, and thermoelectrics. Her particular focus is on using machine learning for efficient computational approaches and for interfacing computational models with materials characterization (x-ray, electron, and scanning probe). She is a senior fellow at the Northwestern Argonne Institute for Science and Engineering, and a fellow of the University of Chicago Consortium for Advanced Science and Engineering. She is also an associate editor at the ACS Journal Chemistry of Materials, a member of the Condensed Matter and Materials Research Committee of the National Academies of Sciences, Engineering, and Medicine, and serves on the advisory boards for the journal APL-Machine Learning, Duke’s aiM-NRT AI training project, and CEDARS EFRC.
Institution/Lab: Argonne National Laboratory
Website: https://www.anl.gov/profile/maria-k-chan
SRP Collaboration Topic/Title: AIMaterials
Field or research area: Computational Materials Science
Please select all the topical areas that apply to your project:
Computational Science Applications (i.e., bioscience, cosmology, chemistry, environmental science, nanotechnology, climate, etc.); Data Science (i.e., data analytics, data management & storage systems, visualization); High-Performance Computing; Machine Learning and AI
Brief Abstract:
We use a combination of AI/ML and computational modeling to understand and design energy materials. In particular, we work on (1) predicting materials properties using a combination of first principles calculations and ML; (2) seeing materials changes in the nanoscale through interpreting x-ray and electron microscopy data; (3) using computer vision and language models to create intelligent knowledge extraction software. These approaches are applied towards energy storage, photovoltaics, or catalysis.
Desired relevant skills, background, or interests:
Proficiency in Python programming is required. Knowledge of some of the following is beneficial: batteries, solar cell, catalysis, solid state physics, first principles or atomistic simulations, machine learning/artificial intelligence, computer vision, language models.
Other comments:
Do any special requirements apply? Permanent Resident OK
Other, specify:
Keywords:
Computational Materials, Machine Learning, Artificial Intelligence, Microscopy. Spectroscopy, Computer Vision, X-ray, Quantum, Solar Cell, Battery, Energy Storage, Sustainability
Lightning Talk Title: Theory-informed AI for Materials Characterization