Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling
CHGNet: pretrained universal interatomic potential to study electron coupled ionic systems
Supercomputing at the exascale and beyond: future trends and challenges
Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications
Gradient-Based Multidisciplinary Design Optimization for Propeller Design | Validation of a multivariate non-Gaussian, non-stationary wind pressure simulation model for performance-based wind engineering
Efficient calculations of electronic structures with machine-learning models
The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research, from […]
The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research, from […]
Active Learning for Physics Informed Data Sampling and Construction of Free Energy Representations
Women in Computational ScienceThe Women in Computational Science Symposium is the inaugural event for MICDE's DISCOVER (Diversity and Innovation in Scientific Computing: Opportunities for Valuing Exploration and Representation) mini-symposium series. This mini-symposium provides a unique opportunity to delve into the pioneering research conducted by women in computational science while also gaining insight into their personal […]