A Framework for Generalization Analysis of Machine-Learned Interatomic Potentials: A Case Study on Crystalline Defects

Yangshuai Wang
April 9, 2024

Lightning Talks for Junior Participants

April 9, 2024

Diverse data generation for machine learning potentials

Danny Perez
April 9, 2024

Learning Together: Towards foundation models for machine learning interatomic potentials with meta-learning

Alice Allen
April 8, 2024

Matrix Function Networks for learning non local quantum effects.

Ilyes Batatia
April 8, 2024

Pyiron: workflows for the development and assessment of interatomic potentials

Jan Janssen
April 8, 2024

Atomic cluster expansion for modeling local and semilocal atomic interactions

Ralf Drautz
April 8, 2024

Physics-Inspired Machine Learning Methods: A Status Report on Predictive Chemistry

Teresa Head-Gordon
April 8, 2024

Exploring Compositional and Configurational Chemical Space with Explainable AI

Alexandre Tkatchenko
March 29, 2024