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Guided learning journeys that build knowledge step by step.
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This learning path guides high school students through the concepts and techniques used to locate stationary points (minima and transition states) on a potential energy surface (PES). Starting with the fundamentals of the PES, it covers energy minimization, the role of gradients and Hessians, convergence criteria, transition state search methods, and intrinsic reaction coordinate (IRC) analysis.
This learning path introduces high school students to the principles of Monte Carlo simulations, focusing on the Metropolis algorithm, Markov chains, canonical ensemble, importance sampling, and the calculation of thermodynamic properties. It builds from foundational statistical mechanics and random sampling to advanced simulation techniques, culminating in practical applications in computational chemistry.
This learning path introduces high school students to the fundamental principles of molecular dynamics (MD) simulations, focusing on integration algorithms, periodic boundary conditions, thermostats, and barostats. It begins with core concepts in classical mechanics and computational chemistry, then builds up to practical simulation techniques.
A structured learning path for high school students to understand the principles of force fields used in molecular mechanics. Starting from classical mechanics and potential energy surfaces, the path covers bonded and non-bonded interactions, including Lennard-Jones and Coulomb potentials, and concludes with an overview of common force field types.
A structured path from foundational quantum chemistry to semi-empirical methods, covering Hückel, Extended Hückel, and modern NDDO-based methods like MNDO, AM1, and PM3, including parameterization and applications.
This learning path introduces the fundamental concepts and methods for describing electron correlation beyond the Hartree-Fock approximation. Starting from the Hartree-Fock method and the definition of correlation energy, it progresses through configuration interaction (CI), Møller-Plesset perturbation theory (MP2), and coupled cluster methods (CCSD, CCSD(T)), highlighting their theoretical foundations, strengths, and limitations.
A systematic learning path covering the core principles of DFT, from quantum mechanics prerequisites to the Hohenberg-Kohn theorems, Kohn-Sham approach, and exchange-correlation functionals. Designed for high school students with an interest in computational chemistry.
This learning path introduces the role and types of basis sets used in computational chemistry, focusing on STO vs. GTO, minimal basis sets, Pople and Dunning basis sets, and polarization/diffuse functions. It assumes prior knowledge of quantum chemistry basics and Hartree-Fock theory, and builds a systematic understanding of how basis sets affect molecular calculations.
This path guides learners from foundational quantum mechanics and computational chemistry to a solid understanding of the Hartree-Fock method. It covers Slater determinants, the Fock operator, Roothaan equations, the self-consistent field procedure, and Koopmans' theorem, emphasizing the logical progression of concepts.
A systematic learning path for high school students with an interest in computational chemistry. It covers the quantum mechanical foundations, the separation of electronic and nuclear motion, the concept of the potential energy surface, and the limitations of the approximation.