Agenda

Machine Learning and AI are fundamentally changing how we explore  chemistry and materials. Foundation models now enable simulations at  unprecedented scale, generative approaches design molecules and  materials on demand, and autonomous systems accelerate discovery through  closed-loop experimentation.

Building on the success of previous MLCM events, we continue creating  a space where senior experts and early-career researchers come together  to share and learn, spark collaborations, and sketch brilliant ideas on  the back of a napkin.

Topics

• Foundation Models for Chemistry and Materials.
• GenAI for Molecular and Materials Design.
• Multi-Modal Learning and AI-enhanced Differentiable Models.
• AI-driven Exploration of Atomistic Spaces and Reactivity.
• Uncertainty Quantification and Trustworthy ML for Chemistry and Materials.
• Agentic AI for Chemical R&D.
• Autonomous Labs and Simulations.
• GPU/TPU-accelerated Computational Chemistry.
• AI/ML-driven Inverse Chemical and Materials Design.

Organizers

Michael G Taylor (LANL T-1) mgt16@lanl.gov

Ajay Khanna (LANL T-1) akhanna2@lanl.gov

Emily Shinkle (LANL CAI-3) eshinkle@lanl.gov

Nikita Fedik (NVIDIA) nfedik@nvidia.com

Organizing Committee

Ben Nebgen (LANL T-1) 

Kipton Barros(LANL T-1)

Ying Wai Li (LANL CAI-1)

Ping Yang (LANL T-1) 

Enrique Batista (LANL T-CNLS)

Nicholas Lubbers (LANL CAI-3)

Danny Perez (LANL XCP-AI4ND)

Sergei Tretiak (LANL T-1/CINT/CNLS)