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Guided learning journeys that build knowledge step by step.
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A comprehensive graduate-level learning path covering the theoretical foundations of phonons in solids, from lattice dynamics and quantum mechanical treatment to computational methods including DFT-based calculations of phonon dispersion, density of states, and thermal transport properties. The path integrates solid state physics and density functional theory concepts, emphasizing practical computational techniques and their applications.
A graduate-level learning path covering the foundations of statistical mechanics and its application to materials science, including ensembles, partition functions, phase transitions, and critical phenomena. Designed for theoretical students with prior knowledge of thermodynamics and quantum mechanics.
This graduate-level path equips materials science students with the knowledge and skills to model kinetic processes such as diffusion, nucleation, growth, and reactions using computational methods. It bridges thermodynamics, statistical mechanics, and programming with practical simulation techniques, culminating in kinetic Monte Carlo methods.
A comprehensive graduate-level learning path covering the theory and practice of multiscale modeling in materials science. It progresses from foundational quantum and atomistic methods through mesoscale techniques to scale-bridging approaches, emphasizing how different computational methods connect across length and time scales.
This advanced graduate-level path equips learners with the knowledge and skills to apply machine learning to materials science challenges, covering property prediction, generative design, high-throughput screening, and machine-learned interatomic potentials. It integrates core ML concepts with materials data and domain-specific workflows, culminating in practical applications.
This advanced graduate-level path systematically develops the theoretical and computational tools needed to understand and apply many-body methods in materials science. It starts with essential quantum mechanics and second quantization, then progresses through Green's functions and many-body perturbation theory (GW approximation), excitonic effects via the Bethe-Salpeter equation, and electron-phonon coupling. The path bridges from DFT as a starting point to advanced many-body techniques, with a focus on physical concepts and practical applications.
A comprehensive graduate-level learning path for mastering advanced DFT methods, including hybrid functionals, DFT+U, GW approximation, van der Waals corrections, and excited-state calculations. It builds from solid-state physics and DFT foundations to advanced many-body techniques, with practical applications in computational materials science.
This advanced learning path equips learners with the knowledge and skills to computationally model surfaces and interfaces using DFT. It covers crystallography foundations, surface thermodynamics, DFT methods, and applications to adsorption, reactions, and heterostructures, culminating in a capstone project.
This path systematically covers the computational modeling of point defects, dislocations, and grain boundaries, focusing on defect energetics and migration. It integrates essential crystallography and DFT knowledge, progressing from foundational concepts to advanced simulation techniques.
This learning path introduces materials students to the principles and practices of materials informatics, focusing on materials databases, data standards, data mining, and informatics tools. It bridges materials science fundamentals with data science techniques to enable efficient data-driven materials discovery.