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Guided learning journeys that build knowledge step by step.
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This learning path equips materials science professionals with the knowledge to navigate ethical dilemmas and sustainability challenges in computational materials research. It covers foundational ethics, environmental impact assessment, sustainable materials design, ethical sourcing, and responsible research practices, culminating in a capstone project to apply these principles.
This path equips materials researchers with the knowledge to design and implement automated discovery workflows. It covers high-throughput screening, AI-driven property prediction, experiment-computation integration, and self-driving laboratories, grounded in essential materials science and machine learning prerequisites.
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.
This learning path equips computational materials researchers with the skills to proficiently use major simulation software (VASP, Quantum ESPRESSO, LAMMPS, Gaussian) and integrate them into research workflows. It covers essential background knowledge, practical software operation, scripting for automation, and workflow management, culminating in a capstone project.
This advanced learning path equips chemists and materials scientists with the knowledge to apply computational chemistry, particularly density functional theory (DFT), to understand and predict materials properties, reactions, and synthesis. It bridges quantum chemistry fundamentals with practical applications in surface chemistry, molecular materials, and reaction mechanisms.
This learning path equips metallurgy professionals with the knowledge and skills to apply computational methods to alloy design, steel modeling, phase transformations, and processing simulations, including welding. It bridges metallurgical principles with computational tools, emphasizing practical applications in industrial R&D.
A professional learning path for applying computational materials science to industrial challenges such as product development, quality control, process modeling, and failure analysis. Learners will move from foundational concepts through simulation workflows and practical applications.
This graduate-level learning path equips environmental and materials students with computational skills to design sustainable materials. It covers foundational sustainability concepts, materials science basics, computational modeling techniques, and life-cycle assessment, culminating in a capstone project on sustainable materials design.
This learning path equips graduate energy students with the computational skills needed to study and design materials for energy applications. It covers essential materials science concepts, quantum mechanical simulation methods, and their application to batteries, fuel cells, thermoelectrics, and photocatalysts.
This advanced graduate-level path equips chemistry and materials science students with the skills to model heterogeneous catalysts using DFT. It progresses from fundamental electronic structure theory and surface modeling to the analysis of active sites, reaction mechanisms, and practical simulation workflows, culminating in a capstone project.