AIMS_2024_workshop

"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

"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).

"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

"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