Environment-adaptive machine learning potentials for atomistic simulations of materials under extreme conditions

Ngoc-Cuong Nguyen
April 9, 2024

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
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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