Awesome

AIMS_2024_workshop

JARVIS AIMS Event page:

Event Page

Agenda:

Agenda

Day 1

Introduction:

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

Session 1:

• "Machine learning electrochemistry", Nicola Marzari (EPFL). Slides PDF
• "Predicting Quantum Monte Carlo Charge Densities using Graph Neural Networks", Abdulgani Annaberdiyev and P. Ganesh (ORNL). Slides PDF
• "Increasing AI/ML Predictions Through DMC-enhanced Delta Learning", Anouar Benali (ANL). Slides PDF
• "Unleashing the Power of Artificial Intelligence for Phonon Thermal Transport", Ming Hu (University of South Carolina). Slides PDF
• "Machine learning models for accelerating materials discovery", Christopher Sutton (University of South Carolina). Slides PDF
• "Accelerating Scientific Discovery in Catalysis with Artificial Intelligence", Hongliang Xin (Virginia Tech). Slides PDF

Session 2:

• "Integrating Autonomous Systems for Advanced Material Discovery: Bridging Experiments and Theory Through Optimized Rewards", Sergei Kalinin (UT Knoxville and PNNL). Slides PDF
• "HPC+AI-enabled Materials Characterization and Experimental Automation", Mathew Cherukara (ANL). Slides PDF
• "Targeted AI-Driven Materials Discovery", Chris Stiles (JHUAPL). Slides PDF
• "Algorithms and opportunities for self-driving laboratories: model-based control, physics discovery, and co-navigating theory and experiments", Rama Vasudevan (ORNL). Slides PDF
• "Theory-informed AI/ML for materials characterization", Maria Chan (ANL).
• "Data-driven approaches to lattice dynamics and vibrational spectroscopy", Yongqiang Cheng (ORNL).

Day 2

Session 3:

• "Data Standards: the key enabler of AI-driven materials science at the nanoscale", Timur Bazhirov (Mat3ra). Slides PDF
• "Chemical Foundation Models for Complex Materials", Vidushi Sharma (IBM). Slides PDF
• "A Practical Guide to Building with LLMs", Eddie Kim (Cohere). Slides PDF
• "Beyond Experimental Structures: Advancing Materials Discovery with Generative AI", Anuroop Sriam (Meta). Slides PDF
• "Accelerating materials design with AI emulators and generators", Tian Xie (Microsoft).
• "Combining machine-learning, physics, and infrastructure to accelerate materials research", Ale Strachan (Purdue).
• "Improving machine learning with polymer physics", Debra Audus (NIST). Slides PDF
• "Integrated Data Science and Computational Materials Science in Complex Materials", Dilpuneet Aidhy (Clemson). Slides PDF

Session 4:

• "Sampling Strategies for Robust MLIPs", Michael Waters (Northwestern). Slides PDF
• "Random Sampling of Chemical Space", Guido von Rudorff (U. of Kassel). Slides PDF
• "Data-driven microstructure-property mapping: the importance of microstructure representation", Olga Wodo (Buffalo). Slides PDF
• "Artificial Intelligence for Materials Geometric Representation Learning and High Tensor Order Property Predictions", Keqiang Yan (TAMU). Slides PDF

Hands-on session:

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

Part-1:

NN-Calculator

Part-2:

2.1 JARVIS_QuantumEspressoColab_Basic_Example

2.2 Analyzing_data_in_the_JARVIS_DFT_dataset

2.3 Basic_ML

2.4 Basic_ALIGNN

2.5 Train_ALIGNNFF_Silicon

2.6 (optional) ALIGNN_Structure_Relaxation

2.7 AtomGPT_example

2.8 (optional) AtomVision

Part-3:

Gaussian_Processes

Active Learning

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

https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks