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Guided learning journeys that build knowledge step by step.
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A systematic learning path covering core classical physics topics—mechanics, electromagnetism, thermodynamics, and waves—with an emphasis on their mathematical descriptions, preparing high school students for computational methods in physics.
This learning path introduces students to the field of computational physics, covering its definition, history, applications, numerical methods, and the standard workflow. It is designed for beginners with basic physics and mathematics knowledge, providing a systematic overview and practical understanding of how computers are used to solve physics problems.
A comprehensive graduate-level learning path covering the full research lifecycle in computational materials science, from foundational materials science and computational methods to literature review, research design, validation, reproducibility, communication, and ethics. Designed for aspiring researchers to develop rigorous, reproducible, and ethically sound research practices.
This graduate-level path explores the computational design of metamaterials, covering the underlying physics of artificial electromagnetic and acoustic structures, numerical simulation methods, and optimization techniques for inverse design. Learners progress from foundational wave theory to advanced topics in photonic and acoustic metamaterials, culminating in practical applications.
A graduate-level learning path for materials researchers to understand the Materials Genome Initiative (MGI), its goals of accelerating materials discovery, and the integrated framework of experimental, computational, and data-driven approaches. It covers the core concepts, data infrastructure, partnerships, and impact, with emphasis on the integration of these elements.
This path guides graduate students through the theoretical and computational foundations required to simulate quantum materials, focusing on strongly correlated systems, quantum magnetism, unconventional superconductivity, and quantum phase transitions. It covers essential quantum mechanics, many-body methods, and practical simulation techniques, culminating in the application of these methods to real quantum materials.
This graduate-level path guides learners through the essential concepts and computational methods for studying topological materials, from solid state physics and DFT to topological invariants and prediction of topological insulators and Weyl semimetals.
This advanced graduate-level path equips learners with the knowledge to computationally discover and characterize novel two-dimensional materials. It covers fundamental concepts of 2D materials, density functional theory, high-throughput screening, and property prediction, culminating in the design of heterostructures. The path emphasizes practical applications and critical evaluation of computational results.
This learning path equips educators with the knowledge and skills to design and deliver effective courses in computational materials science. It covers core simulation techniques, teaching methodologies, and assessment strategies tailored for interdisciplinary STEM education.
A learning path for materials researchers to understand and apply open science principles, including open data, open source tools, and collaborative platforms, with a focus on reproducibility and transparency in computational materials science.