Path Category
正在从 AllPath API 加载 Path Category…
ALLPATHFoundationALLPATHFoundationPath Category
正在从 AllPath API 加载 Path Category…
Path Category
Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7800 条 Path
共 7800 条 Path · 第 20 / 780 页
This learning path guides advanced university students through the principles and practices of computational phase diagram modeling, focusing on CALPHAD methodology, thermodynamic modeling, phase equilibria, diffusion, and phase transformations. It builds from foundational thermodynamics to advanced computational techniques, emphasizing practical applications in materials science.
This path guides materials students from crystallographic fundamentals to computational methods for simulating X-ray diffraction and determining crystal structures. It covers symmetry, space groups, and defects, emphasizing practical applications in materials science.
This advanced learning path guides you from foundational quantum mechanics and solid-state physics through the principles of density functional theory (DFT), band theory, and many-body effects, culminating in the application of advanced methods to compute and interpret band structures, density of states, Fermi surfaces, and quasiparticle properties. You will systematically build the conceptual and mathematical toolkit needed to understand and apply electronic structure methods in computational materials science.
A comprehensive learning path for engineering students to understand and apply finite element methods to materials science problems. It covers continuum mechanics, numerical methods, and specific applications such as elasticity, plasticity, fracture, and heat transfer.
This learning path guides materials science students from foundational statistical mechanics and probability through the Metropolis algorithm to advanced applications in spin systems, phase transitions, and materials-specific Monte Carlo simulations. It emphasizes hands-on programming exercises and conceptual understanding of sampling and equilibration.
This path provides a systematic introduction to molecular dynamics (MD) simulations for materials science. Starting from classical mechanics and statistical mechanics, it progresses through force fields, integration algorithms, ensemble control, and practical simulation workflows, culminating in advanced applications and analysis techniques. The path emphasizes the underlying physics and computational methods, preparing learners to perform and critically evaluate MD simulations of materials.
A comprehensive learning path covering the theoretical foundations, practical implementation, and application of density functional theory (DFT). Starting from quantum mechanics and mathematical prerequisites, it progresses through the Hohenberg-Kohn theorems, Kohn-Sham equations, exchange-correlation functionals, and numerical implementation, culminating in hands-on practice with DFT codes.
This learning path builds a systematic understanding of thermodynamics applied to materials science. It starts with core thermodynamic laws and state functions, progresses through solution models and free energy concepts, and culminates in phase diagrams, phase equilibria, and an introduction to kinetics and thermal properties. Designed for materials students at an intermediate level.
This learning path provides materials science students with the essential quantum mechanics needed to understand atomic structure and bonding. It starts with foundational mathematics and physics, progresses through wave mechanics and the Schrödinger equation, and concludes with applications to atoms and molecules. The path emphasizes the conceptual and mathematical tools required for computational materials science.
A systematic introduction to solid state physics for high school students, covering crystal structures, bonding, lattice vibrations, and electronic band theory. This path builds from basic physics and chemistry concepts to a foundational understanding of how atoms organize into solids and how their structure dictates macroscopic properties.