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Guided learning journeys that build knowledge step by step.
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This learning path introduces graduate students to the principles and applications of quantum computing in chemistry. It covers foundational quantum mechanics and computational chemistry, then progresses to quantum information concepts, quantum algorithms for chemistry, hardware platforms, and current limitations. The goal is to understand the potential of quantum computers to solve chemical problems beyond classical capabilities.
This learning path equips polymer science professionals with the knowledge and skills to apply computational methods—ranging from atomistic molecular dynamics to coarse-grained simulations—to predict polymer dynamics, phase behavior, and mechanical properties. Starting with fundamental polymer physics and simulation techniques, the path progresses through advanced modeling approaches and practical applications, culminating in a capstone project that integrates all concepts.
This advanced graduate-level learning path systematically covers the theoretical foundations, parameterization strategies, optimization techniques, validation protocols, and modern polarizable force field developments essential for designing and refining force fields used in molecular mechanics (MM) and molecular dynamics (MD) simulations. The path emphasizes the integration of quantum mechanical data, statistical mechanics, and practical parameter fitting.
This learning path equips structural chemistry professionals with the computational skills needed to apply crystallographic methods effectively. It covers essential theory, software tools, and practical techniques for crystal structure prediction, powder diffraction analysis, refinement support, and disorder modeling, integrating DFT and computational chemistry approaches.
This learning path equips graduate students in computational chemistry with the knowledge and skills to overcome sampling limitations in molecular dynamics simulations. It covers enhanced sampling techniques including umbrella sampling, metadynamics, replica exchange, and transition path sampling, along with essential prerequisites in statistical mechanics and free energy calculations.
This path equips pharmaceutical researchers with the knowledge and skills to apply molecular docking in drug discovery. It covers the foundational concepts of molecular recognition, protein and ligand preparation, docking algorithms, scoring functions, pose prediction, binding energy estimation, and benchmarking. Learners will gain practical proficiency in setting up, running, and evaluating docking studies for virtual screening and lead optimization.
This learning path equips chemical data professionals with the knowledge and skills to manage, query, and mine chemical databases. It covers foundational cheminformatics concepts (SMILES, InChI, molecular descriptors), major public databases (PubChem, ChEMBL), and essential SQL and data mining techniques, with practical applications in computational chemistry and programming.
This learning path equips chemical industry professionals with the knowledge and skills to apply computational chemistry to real-world challenges, including materials design, process optimization, and intellectual property considerations. Starting with foundational quantum chemistry and molecular modeling, the path progresses through advanced simulation techniques, property prediction, and finally to strategic applications in R&D and IP.
A professional learning path for researchers transitioning to high-performance computing for computational chemistry. It covers Linux and scripting fundamentals, parallel programming with MPI and OpenMP, job schedulers, GPU computing, and performance optimization, culminating in a capstone project.
This learning path teaches university students how to use Python to automate calculations and analyze data in computational chemistry. It covers Python fundamentals, NumPy/SciPy for numerical work, Matplotlib for visualization, parsing output files, and workflow automation, progressing from basic programming to advanced data analysis.