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3.320 Atomistic Computer Modeling of Materials (MIT) 3.320 Atomistic Computer Modeling of Materials (MIT)

Description

Theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Energy models, from classical potentials to first-principles approaches. Density-functional theory and the total-energy pseudopotential method. Errors and accuracy of quantitative predictions. Thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations. Free energies and phase transitions. Fluctuations and transport properties. Coarse-graining approaches and mesoscale models. Theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Energy models, from classical potentials to first-principles approaches. Density-functional theory and the total-energy pseudopotential method. Errors and accuracy of quantitative predictions. Thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations. Free energies and phase transitions. Fluctuations and transport properties. Coarse-graining approaches and mesoscale models.

Subjects

atomistic computer simulations | atomistic computer simulations | Density-functional theory | Density-functional theory | total-energy pseudopotential method | total-energy pseudopotential method | Thermodynamic ensembles | Thermodynamic ensembles | Monte Carlo sampling | Monte Carlo sampling | molecular dynamics simulations | molecular dynamics simulations | Free energies | Free energies | phase transitions | phase transitions | Coarse-graining approaches | Coarse-graining approaches | mesoscale models | mesoscale models

License

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3.320 Atomistic Computer Modeling of Materials (SMA 5107) (MIT) 3.320 Atomistic Computer Modeling of Materials (SMA 5107) (MIT)

Description

This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations; free energy and phase transitions; fluctuations and transport properties; and coarse-graining approaches and mesoscale models. The course employs case studies from industrial applications of advanced materials to nanotechnology. Several laboratories will give students direct experience with simulations of classical force fields, electronic-structure app This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations; free energy and phase transitions; fluctuations and transport properties; and coarse-graining approaches and mesoscale models. The course employs case studies from industrial applications of advanced materials to nanotechnology. Several laboratories will give students direct experience with simulations of classical force fields, electronic-structure app

Subjects

simulation | simulation | computer simulation | computer simulation | atomistic computer simulations | atomistic computer simulations | Density-functional theory | Density-functional theory | DFT | DFT | Hartree-Fock | Hartree-Fock | total-energy pseudopotential | total-energy pseudopotential | thermodynamics | thermodynamics | thermodynamic ensembles | thermodynamic ensembles | quantum mechanics | quantum mechanics | first-principles | first-principles | Monte Carlo sampling | Monte Carlo sampling | molecular dynamics | molecular dynamics | finite temperature | finite temperature | Free energies | Free energies | phase transitions | phase transitions | Coarse-graining | Coarse-graining | mesoscale model | mesoscale model | nanotube | nanotube | alloy | alloy

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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3.320 Atomistic Computer Modeling of Materials (MIT)

Description

Theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Energy models, from classical potentials to first-principles approaches. Density-functional theory and the total-energy pseudopotential method. Errors and accuracy of quantitative predictions. Thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations. Free energies and phase transitions. Fluctuations and transport properties. Coarse-graining approaches and mesoscale models.

Subjects

atomistic computer simulations | Density-functional theory | total-energy pseudopotential method | Thermodynamic ensembles | Monte Carlo sampling | molecular dynamics simulations | Free energies | phase transitions | Coarse-graining approaches | mesoscale models

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

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3.320 Atomistic Computer Modeling of Materials (SMA 5107) (MIT)

Description

This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations; free energy and phase transitions; fluctuations and transport properties; and coarse-graining approaches and mesoscale models. The course employs case studies from industrial applications of advanced materials to nanotechnology. Several laboratories will give students direct experience with simulations of classical force fields, electronic-structure app

Subjects

simulation | computer simulation | atomistic computer simulations | Density-functional theory | DFT | Hartree-Fock | total-energy pseudopotential | thermodynamics | thermodynamic ensembles | quantum mechanics | first-principles | Monte Carlo sampling | molecular dynamics | finite temperature | Free energies | phase transitions | Coarse-graining | mesoscale model | nanotube | alloy

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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https://ocw.mit.edu/rss/all/mit-allcourses.xml

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