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Guided learning journeys that build knowledge step by step.
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7817 Paths · page 606 / 782
This learning path equips chemistry students with practical skills to run computational chemistry calculations using major software packages (Gaussian, GAMESS, ORCA, NWChem) and manage jobs in HPC environments. Starting from essential UNIX/Linux fundamentals, learners progress through input/output file formats, job submission, and result analysis, culminating in the ability to independently plan and execute computational studies.
This learning path provides a systematic, graduate-level journey into relativistic quantum chemistry. Starting with the foundations of special relativity and quantum mechanics, it progresses through the Dirac equation, spin-orbit coupling, and modern relativistic Hamiltonians such as Douglas-Kroll-Hess and ZORA, culminating in applications to heavy-element chemistry. The path emphasizes conceptual understanding and computational methods, preparing learners to apply relativistic methods in research.
This path covers advanced computational methods for treating large molecular systems, including linear-scaling techniques, fragmentation methods, and divide-and-conquer approaches. It starts with foundational quantum chemistry and electronic structure theory, then progresses to advanced methods and their practical applications.
This advanced learning path guides theoretical chemistry students through the fundamental concepts of linear and non-linear response theories, focusing on their applications in computational chemistry. It covers polarizabilities, hyperpolarizabilities, excitation energies, and time-dependent methods, building from quantum mechanics and spectroscopy foundations.
This advanced learning path equips students with the knowledge and skills to compute and interpret IR, Raman, NMR, UV-Vis, ECD, and VCD spectra using quantum chemical methods. It covers the underlying electronic structure theory, property calculations, and practical data interpretation, preparing learners for careers in computational chemistry.
A systematic learning path covering the theoretical foundations, methodological variants, and practical applications of hybrid quantum mechanics/molecular mechanics (QM/MM) methods. Starting from the necessary background in QM and MM, the path progresses through embedding schemes, link atom treatments, and advanced techniques, culminating in the application to enzymatic reactions.
A comprehensive learning path covering the theory and practice of ab initio molecular dynamics (AIMD), including Born-Oppenheimer and Car-Parrinello methods, thermostatting, and applications to chemical reactions and materials. Designed for advanced university students with a background in quantum chemistry and statistical mechanics.
This learning path guides students from foundational quantum mechanics and electronic structure theory through the practical application of quantum chemistry to compute molecular properties. It covers essential concepts such as Hartree-Fock and DFT, basis sets, and then focuses on the calculation and interpretation of IR spectra, NMR shielding, UV-Vis spectra via TD-DFT, molecular orbitals, and population analysis. The path emphasizes both theoretical understanding and practical computational skills.
This learning path guides students specializing in DFT through the hierarchy of exchange-correlation functionals, from local and semi-local approximations to hybrid and double-hybrid methods, including range-separated and van der Waals corrections. It builds a systematic understanding of Jacob's ladder and the theoretical foundations necessary for selecting and applying advanced functionals in computational chemistry.
This learning path guides high school students from quantum mechanics basics to the main computational models for solvent effects: continuum models (PCM, COSMO), explicit solvation, QM/MM, and mixed methods. It emphasizes the physical principles and practical applications, ensuring a solid understanding of how solvents influence molecular properties and reactions.