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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7817 Paths
7817 Paths · page 603 / 782
A beginner-friendly learning path for high school students to identify and describe the primary functions of major cellular organelles. Starting with the basics of cell theory and the differences between cell types, this path systematically covers each organelle's structure and function, emphasizing the interconnectedness of cellular processes.
This learning path guides high school students with basic chemistry knowledge through the essential chemical components and macromolecules that constitute a cell. Starting from chemical foundations, it covers water, carbohydrates, lipids, proteins, and nucleic acids, emphasizing their structures, properties, and functional roles in cellular life.
A systematic path from fundamental coordination chemistry to the 18-electron rule, metal carbonyls, metallocenes, and organometallic complexes with metal-carbon sigma bonds. Covers bonding models, nomenclature, and key compound classes.
This learning path guides high school students from fundamental chromatography concepts through the detailed workings of gas chromatography. It covers instrumentation components, separation theory, method development, and practical applications, culminating in the use of retention indices for compound identification.
This learning path guides high school students through the fundamental principles of absorption spectroscopy, starting with the nature of light and progressing to the Beer-Lambert law and its practical applications. Learners will explore the electromagnetic spectrum, the concepts of transmittance and absorbance, and the operation of a spectrophotometer. The path emphasizes conceptual understanding and mathematical relationships essential for analytical chemistry.
This learning path provides a systematic progression from foundational chemistry and mathematics through quantum chemistry, molecular dynamics, and advanced simulation techniques, culminating in research methodologies including data analysis, reproducibility, and scientific communication. It is designed for graduate students aiming to conduct independent research in computational chemistry.
This learning path equips graduate students in computational chemistry with the knowledge and skills to assess and reduce the environmental footprint of their computational work. It covers energy-efficient algorithms, GPU acceleration, cloud versus local computing trade-offs, CO₂ footprint estimation, and sustainable high-performance computing (HPC) practices, all within the context of computational chemistry research.
This graduate-level path equips learners with the knowledge to understand and apply computational methods for automatically discovering chemical reaction pathways. It covers foundational concepts in computational chemistry, automated search algorithms, reaction network construction, kinetic modeling, and uncertainty quantification, culminating in a practical capstone project.
This path equips graduate students with the theoretical and computational foundations to understand and perform excited-state dynamics simulations, focusing on fewest-switches surface hopping (FSSH), multi-configurational methods like CASSCF, and the role of conical intersections. It bridges quantum chemistry and quantum dynamics, covering essential prerequisites and culminating in practical applications.
This advanced graduate-level path introduces the theory and practice of machine-learned force fields (MLFFs), bridging computational chemistry, machine learning, and molecular dynamics. Learners will explore neural network architectures, descriptor design, training data generation, and active learning strategies, culminating in applications to materials and molecular systems.