PHD
  1. Inorganic chemistry  1.1. Coordination chemistry  1.1.1. Ligand field theory  1.1.2. Crystal field theory  1.1.3. Molecular Orbital Theory for Coordination Compounds  1.1.4. Stability of Coordination Compounds  1.1.5. Electronic spectra of coordination compounds  1.1.6. Isomerism in Coordination Compounds  1.1.7. Kinetics and mechanism of coordination reactions  1.2. Organometallic Chemistry  1.2.1. Metal Carbonyls  1.2.2. Metal alkyl and aryl  1.2.3. Fischer and Schrock carbonaceans  1.2.4. Oxidative Addition and Reductive Elimination  1.2.5. Metal Hydrides  1.2.6. Catalysis by organometallic compounds  1.2.7. Metallocenes and Sandwich Complexes  1.2.8. CH activation  1.3. Solid state chemistry  1.3.1. Crystal structures and lattices  1.3.2. Band theory and electrical properties  1.3.3. Defects in the crystal  1.3.4. Synthesis of solid state materials  1.3.5. Magnetic and optical properties of solids  1.3.6. Superconductivity  1.3.7. Solid state ionics  1.4. Bio-inorganic chemistry  1.4.1. Metalloproteins and enzymes  1.4.2. Metal ion transport and storage  1.4.3. The role of metals in medicine  1.4.4. Bioinspired catalysis  1.4.5. Metal–DNA interactions  1.4.6. Metal Complexes in Medical Science  1.5. Lanthanides and Actinides  1.5.1. Electronic configuration and oxidation states in lanthanides and actinides  1.5.2. Spectral and Magnetic Properties in Lanthanides and Actinides  1.5.3. Separation and Extraction of Lanthanides and Actinides  1.5.4. Coordination chemistry of f-block elements  1.6. Main Group Chemistry  1.6.1. Chemistry of boron compounds  1.6.2. Chemistry of Silicon and Germanium Compounds  1.6.3. Chemistry of Phosphorus and Sulfur Compounds  1.6.4. Organosilicon chemistry  1.6.5. Noble Gas Chemistry
  2. Organic chemistry  2.1. Reaction mechanism  2.1.1. Nucleophilic substitution reactions  2.1.2. Electrophilic addition and substitution reactions  2.1.3. Elimination reactions  2.1.4. Rearrangement reactions  2.1.5. Radical reactions  2.1.6. Pericyclic Reactions  2.1.7. Photochemical reactions  2.2. Stereoscopic  2.2.1. Chirality and optical activity  2.2.2. Structural analysis  2.2.3. Stereoelectronic effects  2.2.4. Asymmetric synthesis  2.2.5. Dynamic Stereochemistry  2.3. Organometallic Chemistry in Organic Synthesis  2.3.1. Organolithium and organomagnesium reagents  2.3.2. Transition metal catalyzed coupling reactions  2.3.3. Palladium-catalyzed reactions  2.3.4. Olefin Metathesis  2.3.5. Gold and silver catalysis  2.4. Natural products chemistry  2.4.1. Alkaloids and terpenoids  2.4.2. Steroids and Flavonoids  2.4.3. Total synthesis of natural products  2.4.4. Biosynthesis of natural products  2.5. Supramolecular Chemistry  2.5.1. Host-Guest Chemistry  2.5.2. Self-assembly and molecular recognition  2.5.3. Supramolecular Catalysis  2.5.4. Molecular Machines
  3. Physical Chemistry  3.1. Thermodynamics  3.1.1. Laws of Thermodynamics  3.1.2. Chemical Potential and Equilibrium  3.1.3. Statistical thermodynamics  3.1.4. Non-equilibrium thermodynamics  3.2. Quantum Chemistry  3.2.1. Schrödinger equation and its applications  3.2.2. Molecular orbital theory  3.2.3. Density functional theory  3.2.4. Post-Hartree–Fock methods  3.3. Chemical kinetics  3.3.1. Reaction rate theory  3.3.2. Mechanism and rate laws  3.3.3. Catalysis and enzyme kinetics  3.3.4. Reaction dynamics  3.4. Spectroscopy and molecular structure  3.4.1. Infrared Spectroscopy  3.4.2. UV-visible spectroscopy  3.4.3. Nuclear Magnetic Resonance Spectroscopy  3.4.4. Mass Spectrometry  3.4.5. Raman Spectroscopy  3.5. Surface and Colloid Chemistry  3.5.1. Absorption and Catalysis  3.5.2. Surfactants and micelles  3.5.3. Colloidal Stability  3.5.4. Nanomaterials and Interfaces
  4. Analytical chemistry  4.1. Chromatography  4.1.1. Gas Chromatography  4.1.2. High Performance Liquid Chromatography  4.1.3. Thin Layer Chromatography  4.1.4. Ion Chromatography  4.2. Electroanalytical techniques  4.2.1. Voltammetry and Polarography  4.2.2. Conductometry and potentiometry  4.2.3. Colometry  4.3. Spectroscopic Methods  4.3.1. Atomic absorption spectroscopy  4.3.2. Fluorescence Spectroscopy  4.3.3. X-ray diffraction  4.3.4. Electron paramagnetic resonance spectroscopy  4.4. Chemometrics  4.4.1. Data processing and statistical methods  4.4.2. Multidisciplinary analysis  4.4.3. Machine Learning in Analytical Chemistry
  5. Theoretical and Computational Chemistry  5.1. Molecular dynamics simulation  5.2. Quantum chemistry methods  5.3. Computational drug design  5.4. Machine Learning in Chemistry  5.5. Ab initio molecular dynamics
  6. Biophysics and Medicinal Chemistry  6.1. Drug discovery and design  6.2. Enzyme kinetics and inhibition  6.3. Chemical biology approach  6.4. Protein-ligand interactions  6.5. Bioorthogonal chemistry
  7. Materials chemistry  7.1. Polymer chemistry  7.1.1. Polymerization mechanism  7.1.2. Functional Polymers  7.1.3. Biodegradable Polymer  7.1.4. Conducting and semiconducting polymers  7.2. Nano Chemistry  7.2.1. Nanoparticles and nanostructures  7.2.2. Carbon-based nanomaterials  7.2.3. Quantum dots  7.3. Energy and Environmental Chemistry  7.3.1. Battery and Fuel Cell Chemistry  7.3.2. Green chemistry and sustainable materials  7.3.3. Photocatalysis

PHD ↓


Theoretical and Computational Chemistry


The field of theoretical and computational chemistry is an intense and rapidly advancing area of study in the field of chemistry. This field integrates the principles of physics, chemistry, and mathematics to provide a comprehensive understanding of chemical systems. It uses computational methods and theoretical models to solve complex chemical problems, which may be very difficult or even impossible to tackle with experimental methods alone.

Introduction

At its core, theoretical chemistry attempts to explain the fundamental nature of molecules and chemical reactions using quantum mechanics and statistical mechanics principles. Computational chemistry involves the practical application of these principles through algorithms and simulations performed on computers. Together, these disciplines provide powerful tools for predicting chemical properties, simulating reactions, and understanding molecular behavior.

Theoretical chemistry

Theoretical chemistry aims to answer important questions about molecular structure, dynamics, and interactions at a fundamental level. The two primary fields within theoretical chemistry are quantum chemistry and molecular mechanics.

Quantum chemistry

Quantum chemistry is based on the principles of quantum mechanics. It provides a microscopic description of matter and explains chemical properties in terms of electronic structure. The Schrödinger equation is the cornerstone of quantum chemistry:

HΨ = EΨ

where H is the Hamiltonian operator, Ψ is the wave function of the system, and E denotes the energy levels. Solving this equation for complex molecules often requires approximations, since exact solutions are usually only possible for the simplest systems, such as the hydrogen atom.

Molecular mechanics

Molecular mechanics offers an alternative to quantum chemistry by using classical physics to model molecular structures. It simplifies atoms as points or spheres and bonds as springs. Interatomic force fields describe the potential energy of a molecule:

E = E_bond + E_angle + E_torsion + E_vdw + E_electrostatic

Here, each term on the right-hand side represents the energy contributions from bonds, angles, torsion, van der Waals interactions, and electrostatics, respectively.

Computational chemistry

Computational chemistry uses algorithms and software to perform theoretical chemistry calculations. The proficiency at solving the Schrödinger equation or simulating molecular dynamics allows chemists to model and predict the properties and behaviors of chemical systems.

Methods in computational chemistry

Several computational methods are used, depending on the accuracy required and the computational resources available:

  • Ab-initio methods: These are based directly on quantum mechanics, without requiring experimental data for input parameters. The most common is the Hartree-Fock method, which simplifies the many-electron problem.
  • Density functional theory (DFT): A popular method that focuses on the electron density rather than the wave function, providing a good balance between accuracy and computational expense.
  • Semi-empirical methods: These methods simplify calculations by incorporating experimental data, making them faster but less accurate than ab initio methods.
  • Molecular dynamics (MD): MD simulations study the physical movements of atoms and molecules by using Newtonian equations to predict time-dependent behavior.

Applications of computational chemistry

Computational chemistry has applications in a variety of areas due to its ability to predict the structure and behavior of chemicals:

  • Materials Science: Simulating materials to understand their properties can lead to designing of new materials with desired functionalities.
  • Drug discovery: Computational simulations can predict the binding affinity of potential drug molecules, simplifying the development process.
  • Catalysis: Computational studies help in the design of catalysts by understanding their mechanism and optimizing their performance.
  • Environmental Chemistry: Modeling environmental processes at the molecular level helps assess chemical effects and degradation.

Visualization of molecular structures and reactions

Visualization tools are important for depicting complex molecular structures and the sequence of reactions in theoretical and computational chemistry. Here are simple visual examples:

H H Chlorine

These simple SVG examples depict two chemical species: a hydrogen atom and a hydrogen chloride molecule. Such visualizations help lay the groundwork for understanding the spatial arrangement of atoms, which is fundamental in the study of chemical reactions.

Challenges and future prospects

Theoretical and computational chemistry is constantly evolving, which presents both challenges and opportunities. One challenge is the significant computation power required for highly accurate simulations. However, developments in high-performance computing and quantum computing offer promising advances.

Another challenge lies in creating comprehensive models that can accurately handle the myriad chemical interactions. As interdisciplinary collaboration grows, breakthroughs in machine learning and artificial intelligence are expected to play a key role in this field.

Future possibilities include more sophisticated techniques that integrate both quantum mechanics accuracy and molecular mechanics efficiency, which will allow chemists to explore even more complex biological and chemical systems.

Conclusion

Theoretical and computational chemistry has revolutionized the way chemists understand molecules and reactions. By providing means to predict properties and simulate processes, it has become a fundamental part of modern chemical research. This field not only complements experimental efforts but also paves the way for new discoveries in various scientific disciplines.


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