Learning

Machine Learning Force Fields for Molecular Dynamics

Molecular Dynamics Simulation Basics → Applications of MLFFs to Materials and Molecules

This advanced graduate-level path introduces the theory and practice of machine-learned force fields (MLFFs), bridging computational chemistry, machine learning, and molecular dynamics. Learners will explore neural network architectures, descriptor design, training data generation, and active learning strategies, culminating in applications to materials and molecular systems.