Learning Path

Introduction to Atomistic Simulation → Assessment: MLP Design and Evaluation

This advanced professional learning path guides researchers through the concepts, methodologies, and practical applications of machine learning potentials (MLPs) in computational materials science. Starting with essential prerequisites in atomistic simulation and machine learning, the path progresses through neural network potentials, Gaussian process regression, force field fitting, and ML-MD integration, culminating in hands-on practice and assessment.