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Guided learning journeys that build knowledge step by step.
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This learning path equips materials and energy students with the knowledge and skills to model and predict photovoltaic performance using computational methods. It bridges solid-state physics, DFT, and carrier dynamics to understand absorption, defects, and efficiency limits in solar cell materials.
This graduate-level learning path equips chemistry and materials students with the theoretical and practical knowledge to apply first-principles computational methods to electrochemical systems. It covers the fundamental concepts of electrochemistry, the application of density functional theory (DFT) to electrode materials and interfaces, and advanced methods for calculating electrode potentials, reaction energetics, and catalytic activity. The path emphasizes the physical assumptions and practical considerations necessary for reliable computational predictions in battery and electrocatalysis research.
A graduate-level learning path for nanotechnology students to understand and apply computational methods for modeling nanomaterials, integrating materials science and density functional theory. It covers foundational quantum mechanics and statistical mechanics, progresses through atomistic simulation techniques, and applies them to nanoparticles, nanowires, nanotubes, and quantum dots, emphasizing size effects and property prediction.
This graduate-level learning path guides materials science students through the essential knowledge and skills for computational modeling of ceramics. It covers ceramic crystal structures, defects, mechanical and thermal properties, interfaces, and the computational methods used to study them, emphasizing the connections between atomistic simulations and macroscopic behavior.
A graduate-level learning path for materials science students to systematically understand and apply computational methods for polymers. It covers polymer science foundations, molecular modeling techniques, simulation methods for chain dynamics, and prediction of mechanical properties, integrating necessary chemistry and programming knowledge.
This graduate-level path equips metallurgy students with the knowledge and skills to apply computational methods in alloy design. It covers essential thermodynamics, phase stability prediction, mechanical property modeling, corrosion behavior, and high-entropy alloy design, culminating in an integrated computational design project.
A graduate-level learning path for materials researchers to master data analysis techniques, from Python programming and statistics to advanced clustering and pattern recognition, applied to materials datasets. The path emphasizes hands-on practice and real materials data applications.
This learning path equips materials researchers with the knowledge and skills to design and execute high-throughput computational screening workflows. It covers essential DFT concepts, automation, workflow design, database integration, and ranking methods, culminating in a capstone project.
A graduate-level learning path covering the quantum mechanical and solid state physics foundations needed to understand and predict mechanical, electronic, optical, magnetic, and transport properties of materials. It progresses from fundamental quantum mechanics and crystallography through electronic structure theory to property-specific applications, emphasizing the theoretical connections between microscopic physics and macroscopic behavior.
This graduate-level path builds the theoretical framework needed to understand and predict mechanical, electronic, optical, magnetic, and transport properties of materials from first principles. It starts with quantum mechanics and solid state physics, then develops the many-body and computational methods used in modern materials theory.