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Free Download Computational Chemistry Concepts, Theories And Applications Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.35 GB | Duration: 13h 18m Ab initio methods, Density Functional Theory methods What you'll learn Understanding the undelying theories of various computational methods such as ab initio, density functioanl theory, semi-empirical and molecular mechanics Understanding the difference between wave function and density based methods in computational chemistry and their pros and cons Understanding the cost and accuracy of various methods and basis sets Learn to apply effective and time saving approaches to solve chemical problem with high accuracy and minimum cost (time) Gain knowledge of different resources/databases useful for theoretical chemist Requirements Some knowledge of mathematics is needed as the course contains several equations Description Computational Chemistry involves application of numerical methods for solving the problems related to chemical systems. Mastering in computational chemistry involves not only hands on practice of Computational software, but also requires understanding the underlying theory, computational methods and approaches to solve chemical problems. In this course, students will learn the theoretical framework of computational chemistry methods necessary for understanding of methods. Practical understanding of the strengths, weaknesses, and ranges of applicability of different methods is also presented in this course. This knowledge will allow for the critical evaluation of the validity and accuracy of results and of the conclusions derived from the computational chemistry modelling of chemical problems. Finally, description of a few properties is also given which will give students an idea like how the properties are calculated through computational tools.The following topics will be discussed in this course:· Potential Energy Surface· Minima and Saddle Points· Thermodynamics and Normal Mode Analysis· Schrodinger Wave Equation· Molecular Hamiltonian and Born-Oppenheimer Approximation· Hartree-Fock Method· Post Hartree-Fock Methods· Static and Dynamic Correlation· Density Functional Theory· Basis Functions and Basis Sets· Excited States· Restricted and Open Shell Systems· Cost and Accuracy· Strategies to Reduce Cost of Computational methods· Molecular Mechanics· Semi-Empirical Methods· Properties Calculations Overview Section 1: Introduction Lecture 1 Introduction of the course, computational chemistry methods Section 2: Potential energy surface (PES) Lecture 2 PES of N2 molecule and ozone Lecture 3 Hypersurface Section 3: Minima and Saddle points Lecture 4 Newton Raphson Method Lecture 5 Finding and characterizing stationary points Section 4: Nomal mode anlysis, thermal correction to energies Lecture 6 Normal mode analysis Lecture 7 Partition functions Lecture 8 different partition functions in energy and entropy Section 5: Schordinger Equation and postulates of quantum mechanics Lecture 9 Postulates of quantum Mechanics Section 6: Molecular Hamiltonian and Born-Oppenheimer Approximation Lecture 10 Molecular Hamiltonian and Born-Oppenheimer Approximation Lecture 11 Many body problem and Variational approach Section 7: Hartree Fock Method Lecture 12 Contruction and optimization of trial wavefunction, Overlap and Resonance Integ Lecture 13 Hartree Product, constraints of trial wavefunction Lecture 14 Slater determinent wavefunction and Hartree Fock calculations Lecture 15 Hartree Fock Energy Lecture 16 SCF Procedure and Hartree Fock equation Section 8: Static and Dynamic correlation, and Post Hartree Fock Methods Lecture 17 Dynamic correlation and multideterminent wavefunction Lecture 18 Perturbation Theory part 1 Lecture 19 Perturbation theory Part II, advantages and disadvantages Lecture 20 Coupled Cluster post HF methods Lecture 21 Static Correlation and Methods to capture Static Correlation Section 9: Density Functional Theory Lecture 22 DFT basic and the fundamental theorems such as Hohenberg-Kohn, Thomas Fermi Lecture 23 Kohn Shame Theorem of DFT Lecture 24 Exchange Correlation Functionals Lecture 25 Classes of DFT methods and their functionals Section 10: Basis set and Basis function Lecture 26 Basis function Lecture 27 Basis set Lecture 28 Types of basis set and polarization function Lecture 29 Diffuse functions, and the choice of basis sets Lecture 30 plane wave basis sets Section 11: Cost and Accuracy Lecture 31 Cost and accuracy of methods Lecture 32 Errors in geometries and energies of different methods Lecture 33 Strategies to reduce computational cost Lecture 34 solvation models and their associated costs Lecture 35 Multilayer method Section 12: Excited states Lecture 36 Configuration Interaction singles Lecture 37 Time dependent DFT Section 13: Force Field methods Lecture 38 Force Fields overview Lecture 39 Bond stretching terms in force fields Lecture 40 Bending, Torsion and non-bonding terms. Lecture 41 steps in Force fields Section 14: Semi-empirical methods Lecture 42 Semi-empirical methods Huckel Theory Lecture 43 Complete Neglet of Differential Overlap (CNDO) Lecture 44 Intermediate Neglect of Differential Overlap and NNDO Section 15: Properties Calculations Lecture 45 Properties and Natural Bonding Orbitals Lecture 46 Multipole moments and Molecular Electrostatic Potential Lecture 47 IR and Raman spectra Lecture 48 UV-Vis and NMR spectra All those scientists who intend to learn/apply quantum mechanis or molecular mechanics based methods to their research Screenshot Homepage https://www.udemy.com/course/computational-chemistry-concepts-theories-and-applications/ Rapidgator https://rg.to/file/06589ef7b33bea1d9dcf2b25553535ab/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part2.rar.html https://rg.to/file/7e8010c504dacd0f714d36c1cdab0946/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part3.rar.html https://rg.to/file/a68f5489381f3c467fc83da02a0085f4/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part4.rar.html https://rg.to/file/ef1e3beb82cb771edce52a4252503904/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part1.rar.html Fikper Free Download https://fikper.com/CyibhnUlai/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part1.rar.html https://fikper.com/To1woht5zF/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part2.rar.html https://fikper.com/VhR7kEBibd/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part3.rar.html https://fikper.com/uu58XS8tZ5/awijo.Computational.Chemistry.Concepts.Theories.And.Applications.part4.rar.html No Password - Links are Interchangeable

Bayesian Computational Analyses with R WEBRip | MP4/AVC, ~752 kb/s | 1280 x 720 | English: AAC, 61.8 kb/s (2 ch), 44.1 KHz | 3.41 GB Genre: Business / Data & Analytics | Language: English | +Project Files Learn the concepts and practical side of using the Bayesian approach to estimate likely event outcomes. is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences about the likely parameters of the population. In contrast, the Bayesian approach uses both likelihood functions and a sample of observed data (the 'prior') to estimate the most likely values and distributions for the estimated population parameters (the 'posterior'). The course is useful to anyone who wishes to learn about Bayesian concepts and is suited to both novice and intermediate Bayesian students and Bayesian practitioners. It is both a practical, "hands-on" course with many examples using R scripts and software, and is conceptual, as the course explains the Bayesian concepts. All materials, software, R scripts, slides, exercises and solutions are included with the course materials. It is helpful to have some grounding in basic inferential statistics and probability theory. No experience with R is necessary, although it is also helpful. The course begins with an introductory section (12 video lessons) on using R and R 'scripting.' The introductory section is intended to introduce RStudio and R commands so that even a novice R user will be comfortable using R. Section 2 introduces the Bayesian Rule, with examples of both discrete and beta priors, predictive priors, and beta posteriors in Bayesian estimation. Section 3 explains and demonstrates the use of Bayesian estimation for single parameter models, for example, when one wishes to estimate the most likely value of a mean OR of a standard deviation (but not both). Section 4 explains and demonstrates the use of "conjugate mixtures." These are single-parameter models where the functional form of the prior and post are similar (for example, both normally distributed). But 'mixtures' imply there may be more than one component for the prior or posterior density functions. Mixtures enable the simultaneous test of competing, alternative theories as to which is more likely. Section 5 deals with multi-parameter Bayesian models where one is estimating the likelihood of more than one posterior variable value, for example, both mean AND standard deviation. Section 6 extends the Bayesian discussion by examining the estimation of integrals to estimate a probability. Section 7 covers the application the Bayesian approach to rejection and importance sampling and Section 8 looks at examples of comparing and validating Bayesian models. What are the requirements? Students will need to install R and RStudio software, but ample instruction for doing so is provided in the course materials. What am I going to get from this course? Over 79 lectures and 11.5 hours of content! Understand Bayesian concepts, and gain a great deal of practical "hands-on" experience creating and estimating Bayesian models using R software. Effectively use the Bayesian approach to estimate likely event outcomes, or probabilities, using their own data. Be able to apply a range of Bayesian functions using R software in order to model and estimate single parameter, multi-parameter, conjugate mixture, multinomial, and rejection and importance sampling Bayesian models. Understand and use both predictive priors and predictive posteriors in Bayesian applications. Be able to compare and evaluate alternative, competing Bayesian models. What is the target audience? The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes. The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modeling. It is useful to have a working knowledge of either basic inferential statistics or probability theory. 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