AIMS_2025_workshop

Event Page and Agenda:

Videos and Agenda

Introduction:

• "Opening Remarks", Jim Warren (NIST). Slides PDF
• "Welcome and Logistics", Daniel Wines (NIST). Slides PDF

Session 1:

• Arun Mannodi-Kanakkithodi (Purdue): "Rational Computational Design of Next-Generation Semiconductors." Slides PDF
• Ankit Agrawal (Northwestern): "Artificial Intelligence for Accelerating Materials Science and Engineering: Leveraging GNNs, LLMs, XAI, Nanocombinatorics, and more." Slides PDF
• Olexandr Isayev (CMU): "Artificial Intelligence (AI) Solutions for Computational and Organic Chemistry." Slides PDF
• Simon J.L. Billinge (Columbia): "Revisiting material structure in the time of AI." Slides PDF
• Roberto Car (Princeton): "Bottom-up Ab-initio Multiscale Modeling of Materials with Machine Learning." Slides PDF
• Tess Smidt (MIT): "Applications of Euclidean Neural Networks for the Understanding and Design of Atomistic Systems." Slides PDF

Session 2:

• Benji Maruyama (AFRL): "Accelerated Research through Autonomous Experimentation/Self-driving Labs."
• Ichiro Takeuchi (UMD): "Self-navigating thin film laboratory: real-time AI-driven optimization of functional thin films."
• Panchapakesan Ganesh (ORNL): "Towards Theory-in-the-loop for Autonomous Experiments–workflows, ML models and ab initio developments leveraging extreme scale computations." Slides PDF
• Joseph Krause (Radical AI): "Radical AI: A New Form of Science."
• Jia-Mian Hu (Wisconsin): "Graph-based Microstructure Informatics."
• Olga S. Ovchinnikova (Thermo Fisher Scientific): "Data Harmonization and Hardware Integration for Labs of the Future.”

Day 2

Session 3:

• Heather Kulik (MIT): "Leveraging experimental data in machine learning and screening to get from computational model to real world materials fast."
• Aditya Nandy (UCLA): "Using Text-Mining, Community Knowledge, and Generative Modeling to Quantify and Engineer Stability in MOFs." Slides PDF
• Corey Oses (JHU): "High-Entropy Oxides and Halides: Expanding the Energy-Materials Space."
• Ali Hamze (Samsung): "GEARS H: Accurate machine-learned Hamiltonians for next-generation device-scale modeling."
• Luis Barroso-Luque (Meta): "Exploring the Frontier of Universal Machine Learning Potentials Part 1: Insights from OMat24 and eSEN." Slides PDF
• Brandon Wood (Meta): "Exploring the Frontier of Universal Machine Learning Potentials Part 2: Insights from OMol25 and UMA." Slides PDF
• Steven Torrisi (Toyota): "Extracting Insights from Atomistic and Spectroscopic Materials Data." Slides PDF

Session 4:

• Milad Abolhasani (NC State): "Data-Rich Autonomous Labs for Accelerated Materials and Molecular Discovery."

• Shengyen Li (NIST): "Toward the Verification, Validation, and Uncertainty Quantification Strategy for Simulations for Additive Manufacturing." Slides PDF

• Nathan Johnson (ZEISS): An Autonomous, Large Language Model Driven X-ray Microscope."

Hands-on session:

Overview Slides for Hands-on, Brian DeCost, Daniel Wines, Kamal Choudhary (NIST). Slides PDF

JARVIS-DFT:

Analyzing_data_in_the_JARVIS_DFT_dataset

ALIGNN:

Basic_ALIGNN

AtomGPT:

AtomGPT_example

DiffractGPT_example

MicroscopyGPT_example

Machine Learning Force Fields:

Intro to GNN force fields (open in colab)

CHIPS-FF:

CHIPS_FF_example

Additional Reference:

JARVIS-Tools-Notebooks, the largest collection of materials design notebooks:

https://github.com/atomgptlab/jarvis-tools-notebooks

JARVIS Database

https://jarvis.nist.gov/