Learning

7817 Paths · page 605 / 782

• Computational Prediction of Chemical Toxicityactive

  This advanced professional learning path equips chemical safety professionals with the knowledge and skills to predict chemical toxicity using computational methods. It covers the foundational chemistry and toxicology concepts, core computational approaches like QSAR and read-across, machine learning techniques, and the regulatory frameworks governing their use, culminating in the practical application for REACH compliance.

  id: 1786slug: clp-1788567828654-ekeh1cupdated: 2026-09-07

• Computational Methods for Atmospheric Reactionsactive

  This graduate-level learning path equips learners with the knowledge and skills to apply computational chemistry methods—particularly kinetics and density functional theory (DFT)—to study atmospheric reactions. It covers essential chemical kinetics, quantum chemistry foundations, reaction mechanisms, spectroscopy, and their integration into global models with climate implications. The path culminates in practical case studies that integrate these concepts.

  id: 1785slug: clp-1788567813417-au6x9bupdated: 2026-09-07

• Computational Green Chemistry and Sustainable Process Designactive

  This advanced professional learning path equips sustainability professionals with computational skills to design environmentally friendly chemicals and processes. It covers green chemistry principles, computational modeling for reaction optimization, solvent selection, toxicity prediction, and life cycle assessment. Learners will integrate these tools to make data-driven decisions that reduce environmental impact.

  id: 1784slug: clp-1788567795602-ir43iuupdated: 2026-09-07

• Machine Learning for Molecular Simulation and Property Predictionactive

  This advanced graduate-level path equips learners with the skills to apply machine learning to accelerate molecular simulations and predict molecular properties. It covers essential computational chemistry and ML foundations, then dives into neural network potentials, QSPR models, active learning, and generative models, emphasizing practical implementation and data-driven discovery.

  id: 1783slug: clp-1788567779073-1by6u5updated: 2026-09-07

• Computational Photochemistry: From Excited States to Non-Adiabatic Dynamicsactive

  This graduate-level path guides learners through the theory and practice of computational photochemistry, covering electronic structure methods for excited states, potential energy surfaces, conical intersections, and non-adiabatic dynamics simulations. Starting from quantum chemistry fundamentals, it progresses to advanced simulation techniques, emphasizing practical applications and hands-on exercises.

  id: 1782slug: clp-1788567760455-xgfsq1updated: 2026-09-07

• Computational Methods for Biomolecular Systemsactive

  A comprehensive learning path for applying computational chemistry techniques to biomolecular systems, covering molecular dynamics, quantum mechanics/molecular mechanics, and related methods. It guides learners from foundational chemistry and physics through advanced simulation techniques, culminating in applications to protein folding, ligand binding, enzyme catalysis, and membrane simulations.

  id: 1781slug: clp-1788567744750-3m6dzlupdated: 2026-09-07

• Computational Materials Design with DFT and Data-Driven Methodsactive

  This path equips learners with the skills to use computational methods for materials design, focusing on density functional theory (DFT) for property prediction and data-driven approaches for high-throughput screening and inverse design. It progresses from fundamental quantum chemistry and solid-state physics to practical database mining and machine learning applications, culminating in a capstone project.

  id: 1780slug: clp-1788567722696-higlihupdated: 2026-09-07

• Computational Methods for Catalyst Studyactive

  This learning path equips university students with the skills to apply density functional theory (DFT) and surface chemistry to study catalysts. It covers the essential theory, surface modeling, adsorption, reaction energetics, transition state search, and descriptor analysis, culminating in a practical case study.

  id: 1779slug: clp-1788567706869-dyezv1updated: 2026-09-07

• Computational Drug Design: From Molecular Modeling to Virtual Screeningactive

  This advanced learning path equips students with the knowledge and skills to apply computational methods in drug design. It covers molecular modeling, structure-based and ligand-based approaches, including molecular docking, virtual screening, QSAR, pharmacophore modeling, and ADMET prediction, culminating in an integrated virtual screening project.

  id: 1778slug: clp-1788567694129-fagwt0updated: 2026-09-07

• Molecular Visualization and Computational Data Analysisactive

  This learning path guides students from programming and data handling basics to advanced molecular visualization and computational chemistry analysis. It covers Python/R scripting, data extraction, plotting, and tools like VMD, Avogadro, and ChemCraft, culminating in a project that integrates these skills.

  id: 1777slug: clp-1788567679182-cerlixupdated: 2026-09-07