CHGNet: A Pre-trained Universal Interatomic Potential for Electron-Coupled Ionic Systems
Offered By: MICDE University of Michigan via YouTube
Course Description
Overview
Explore a comprehensive lecture on CHGNet, a groundbreaking pre-trained universal interatomic potential for studying electron coupled ionic systems. Delve into the challenges of large-scale simulations involving complex electron interactions and discover how CHGNet addresses these issues. Learn about the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph-neural-network-based machine-learning interatomic potential that models the universal potential energy surface. Understand how CHGNet is pretrained on data from the Materials Project Trajectory Dataset, incorporating energies, forces, stresses, and magnetic moments from approximately 1.5 million inorganic structures. Examine the potential of CHGNet to accurately represent electron orbital occupancy and its enhanced capability to describe both atomic and electronic degrees of freedom. Explore various applications of CHGNet in solid-state materials and energy storage, demonstrating its practical significance in the field of atomistic modeling.
Syllabus
B. Deng: CHGNet, pre-trained universal interatomic potential to study electron coupled ionic systems
Taught by
MICDE University of Michigan
Related Courses
Molecular Dynamics for Computational Discoveries in ScienceUniversity of Massachusetts Boston via Independent Моделирование биологических молекул на GPU (Biomolecular modeling on GPU)
Moscow Institute of Physics and Technology via Coursera Numerical Methods And Simulation Techniques For Scientists And Engineers
Indian Institute of Technology Guwahati via Swayam Foundations of Computational Materials Modelling
Indian Institute of Technology Madras via Swayam Fundamentals of Spectroscopy
Indian Institute of Science Education and Research, Pune via Swayam