Maria K. Chan

Maria Chan is a scientist at the Center for Nanoscale Materials at Argonne National Laboratory who studies nanomaterials and renewable energy materials, including solar cells, batteries, thermoelectrics, and catalysts. Her particular focus is on using artificial intelligence/machine learning (AI/ML) for efficient materials property prediction and for interfacing modeling with x-ray, electron, and scanning probe characterization. She also works on using AI for extracting microscopy and spectroscopy data from scientific literature and for microscopy data management.

Chan 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 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, and serves on the advisory boards for the journal APL-Machine Learning, Duke’s aiM-NRT AI training project, and CEDARS EFRC. She was elected a Fellow of the American Physical Society in 2024.

Summer/Fall 2025: We are hiring postdocs and students! Please email me at mchan@​anl.​gov for details.

Education

• BSc, Physics and Applied Mathematics, University of California at Los Angeles
• PhD, Physics, Massachusetts Institute of Technology

Research topics

• First principles and atomistic modeling of nanomaterials and renewable energy materials, including photovoltaics, batteries, photo- and electro-catalysts, and thermoelectrics
• Development of first principles and AI/ML approaches for materials property prediction
• Combined theory and AI/ML approaches and software development for integrating atomistic modeling and experimental characterization measurements

Current/previous projects

• Center for Nanoscale Materials, Theory and Modeling group: structural and electronic properties of semiconductor nanoparticles, transition-metal dichalcogenides for catalysis, hybrid perovskite photovoltaics, connecting modeling to synchrotron characterization and microscopy techniques
• DOE Energy Storage Research Alliance (ESRA): DOE Energy Storage Hub, Materials Acceleration Platform crosscut deputy leader.
• DOE Early Career Award: development of FANTASTX (Fully Automated Nanoscale To Atomistic Structures from Theory and eXperiments) for determination of structures from x-ray, electron, and scanning probe characterization
• Midwest Integrated Center for Computational Materials: inter-operable computational modeling codes to model transport at interfaces
• AI/ML at Scientific User Facilities: use of artificial intelligence and machine learning for information extraction from x-ray, electron microscopy, and neutron data
• DOE SBIR/STTR for data management framework, computer vision, and natural language processing towards labeled microscopy data
• Center for Electrochemical Energy Science: high capacity lithium-ion, lithium-oxygen, and hybrid lithium-ion/lithium-oxygen battery materials, solid-electrolyte interphase, surface and interfacial interactions
• DOE EERE Solar Energy Technology Office: grain boundaries and dopants in CdTe photovoltaics, degradation in Si photovoltaics
• DOE ARPA-E SHIELD: nanostructured thermal barrier coatings
• NSF MRSEC: Orchestrated Iontronics via Dynamic Hybrid Ionic/Electronic Conductors
• NSF CCI: MOSAIC: Multimodal Observatory for Single Atom Imaging of Chemistry
• Argonne internal projects: computer-vision guided retrieval of microscopy images, intermediate band hybrid perovskite photovoltaics, non-equilibrium thermal transport, integrated imaging to understand photocatalysts, non-PGM catalysts
• Industrial sponsors: electronic and lattice thermal transport modeling

Publications

Google Scholar profile

Codes

• https://​github​.com/​M​a​t​e​r​i​a​lEyes
• https://​github​.com/​j​o​s​h​-​m​u​t​i​a​n​/​g​r​a​i​n​-​b​o​u​n​d​a​r​y​-​genie

Data

Published datasets at the Materials Data Facility

Lectures / seminars / podcasts:

• 2025 ML for Materials Structures, on the Carry the Two Podcast from The Institute for Mathematical and Statistical Innovation
• 2023 Nanoscale Structures in Batteries from Theory, AI/ML, and Microscopy/Spectroscopy Experiments at Clean Energy Forum
• 2022 Artificial Intelligence in Computational Materials Science MRS Webinar
• 2021 Modeling and Machine Learning of X-ray Absorption Spectra for Energy Materials: XAS Journal Club
• Tutorial on machine learning of impurity levels by former postdoc Arun Mannodi Kannakithodi
• Cell Press Webinar ​“Machine learning impurity levels in semiconductors”
• Argonne Outloud presentation ​“From Atoms to Clean Energy Technologies” 

Awards

• 2024 American Physical Society Fellow
• 2022 Sam Bader Award
• 2021 Physical Science & Engineering Directorate Excellence Award
• 2020 DOE Early Career Award
• 2017 Argonne Outstanding Postdoctoral Supervisor Award

Research News

• 2024 EXSCLAIM tool for extracting labeled microscopy & spectroscopy data from literature
• 2023 Artificial Intelligence for Materials Discovery (Communications of the ACM)
• 2022 Staff Spotlight
• 2022 Boundaries in NMC battery materials
• 2022 Perovskite design
• 2021 CdTe solar cell
• 2021 Borophane
• 2021 Validating first principles molecular dynamics simulations
• 2019 Ultrafast dynamics in perovskite halides
• 2018 Low Pt catalyst for fuel cells
• 2016 Machine learned interatomic potential for IrO2
• 2013 Hybrid Li-ion/Li-O2 batteries

Current postdoctoral/predoctoral associates

• Dr Chaitanya Kolluru (Google Scholar)(former PhD student co-supervised with Professor Richard Hennig)
• Dr Nina Andrejevic (Maria Goeppert Mayer Fellow)(Google Scholar)(LinkedIn)
• Dr Yiming Chen (LinkedIn)(former PhD student co-supervised with Professor Shyue Ping Ong)
• Dr Tanjin He (Google Scholar)(LinkedIn)
• Dr Jithin George (website)(co-supervised with Drs Valerie Taylor, Pierre Darancet, and Emil Constantinescu)
• Dr Haili Jia (LinkedIn)

Current doctoral student

• Mr Shinjan Dutta (Northwestern University, co supervised with Aggelos Katsaggelos)

Current research assistants

• Ms Yuxin Chang (University of Toronto)
• Ms Taniya Givens (UIUC)
• Mr Justin Potts (Macalester College)
• Mr Len Washington III (Illinois Institute of Technology)
• Mr Jake DeResis (Northwestern University)
• Mr Kailliou Macon-Goudeau (Davidson College)

Former postdoctoral associates

• Dr Alper Kinaci (Google Scholar)(LinkedIn)(Now Senior Computational Specialist at Northwestern University)
• Dr Fatih Sen (Google Scholar)(LinkedIn)(Now Senior Scientist, Metallurgy and Modeling at Novelis)
• Dr Liang Li (Google Scholar)(LinkedIn) (Now Founding Engineer @ Novel)
• Dr Yi Xia (Google Scholar)(Now postdoctoral fellow at Northwestern University)
• Dr Kendra Letchworth-Weaver (Rahman Named Fellow)(Google Scholar)(LinkedIn)(Now Assistant Professor at James Madison University)
• Dr Denise Ford (Duke University)
• Dr Ji-Sang Park (Website)(Now an Assistant Professor at Kyungpook National University)
• Dr Arun Mannodi Kanakkithodi (Google Scholar)(LinkedIn)(Website)(Now Assistant Professor at Purdue University)
• Dr Joydeep Munshi (Google Scholar)(Now Research Scientist at GE)
• Dr Arun Baskaran (Google Scholar)(Now Research Scientist at Dow)
• Dr Davis Unruh (Google Scholar)(Now Research Scientist at Samsung)
• Dr Joshua Paul (Google Scholar)(Now Research Engineer at Boeing)
• Dr Luqing Wang (Google Scholar)(Now Assistant Professor at University of Nevada Las Vegas)

Former doctoral students

• Dr Chris Buurma (Now Modeling and Simulation Scientist at Battelle)
• Dr Joseph Kubal (visiting from Purdue University)(Now Assistant Chemical Engineer at Argonne National Lab)
• Dr Lynza Sprawl (visiting from Oregon State University)(Now Senior R&D Engineer at Honeywell)
• Dr Mingren Shen (University of Wisconsin Madison, co supervised with Professor Dane Morgan)(Now Software Development Engineer at Amazon)
• Dr Eric Schwenker (Northwestern University, co supervised with Professor Chris Wolverton)(Now Computer Vision Engineer at Path Robotics, Inc.)
• Dr Weixin Jiang (Northwestern University, co supervised with Professor Ollie Coissart)(Now Machine Learning Engineer at Uber)
• Dr Sam Tetef (University of Washington, co supervised with Gerald Seidler)

Former research assistants

• Ms Karen Zheng (UIUC) (Now a Food Scientist)
• Ms Jessi Hartman (UCDavis) (Now PhD student at UIUC)
• Ms Amy Wey (Cornell University) (Now Software Engineer at Galaxy Digital)
• Mr Mutian (Josh) Liu (University of Chicago)(Now Senior Software Engineer at Meta)
• Mr Ryan Pencak (Bucknell University) (Now Software Engineer at NYDIG)
• Mr Michael Toriyama (UIUC) (2018 Barry Goldwater Scholar)(Now PhD student at Northwestern)
• Mr Thierry Wambo (University of Texas San Antonio) (Now Data Scientist at Lockheed Martin)
• Mr Ryan Pederson (Virgnia Tech) (Now PhD student at UC Irvine)
• Ms Grace Lu (Northwestern University) (Now PhD student at UIUC)
• Mr Jackson O’Donnell (LinkedIn)(University of Chicago) (Now PhD student at UC Santa Cruz)
• Mr Spencer Hills (LinkedIn)(Wheaton College) (Now medical student)
• Mr Isaac Malsky (University of Chicago) (Now PhD student at University of Michigan)
• Ms Sarah O’Brien (Northwestern University) (Now Cloud Engineer at Sonos)
• Mr Viraaj Jayaram (University of Chicago) (Now PhD student at Yale)
• Ms Sankhya Hirani (UIUC) (Now PhD student at U Washington)
• Mr Rahim Raja (University of Chicago)
• Mr Marcel Chlupsa (Kansas State University)(Now PhD student at University of Michigan)
• Mr Trevor Spreadbury (MIT)(Now Software Engineer at Data Science Institute (DSI) at the University of Chicago)
• Mr Buduka Ogonor (University of Chicago)(Now PhD student at Northwestern University)
• Ms Victoria Adebayo (Howard University)(Now PhD student at Harvard University)
• Mr Justin Pothoof (University of Washington)
• Ms Annie Xu (University of Washington)
• Mr David Flores (Penn State University)
• Mr Thiago da Silva (Boise State University)