Stan Moore - Optimizing GPU Performance: Case Study Using Chain Benchmark in LAMMPS

M. Grazia Speranza - How can we model equity? - IPAM at UCLA

Stan Moore - Optimizing GPU Performance: Case Study Using Chain Benchmark in LAMMPS

Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA

Richard Hennig & Jason Gibson - AI-driven workflows for the discovery of novel superconductors

James Corbett - Flux: a next generation resource manager for HPC and beyond - IPAM at UCLA

Juliane Mueller - Adaptive Computing and multi-fidelity learning - IPAM at UCLA

Dilworth Dula Parkinson - X-ray micro-tomography at the Advanced Light Source & NERSC superfacility

Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA

Aurora Clark - high-dimension perspective on extracting & encoding information in chemical systems

Ralf Drautz - From electrons to the simulation of materials - IPAM at UCLA

Joshua Schrier - Creating Complex Scientific Workflows that Reach into the Real World - IPAM at UCLA

Maxim Ziatdinov - From Human-Centric to AI-Driven Experimentation Workflows for Materials

Alex Aiken - Task-Based Programming with Legion - IPAM at UCLA

Jan Janssen - pyiron – Rapid-prototyping and Up-scaling Workflows for the Exascale - IPAM At UCLA

Samuel Blau - High-Throughput DFT and Monte Carlo for Reaction Networks and Machine Learning

Deborah Bard - The Superfacility Model for Connected Science - IPAM at UCLA

Amit Acharya - Slow time-scale behavior of fast microscopic dynamics - IPAM at UCLA

Frederic Legoll - Parareal algorithms for molecular dynamics simulations - IPAM at UCLA

Boris Kozinsky - Uncertainty-aware machine learning models of many-body atomic interactions

Marisol Koslowski - Surrogate Hot-spot Models for Simulations of Detonation in Energetic Materials