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 ↓


Ab initio molecular dynamics


Ab initio molecular dynamics (AIMD) is an important concept in the field of theoretical and computational chemistry, especially when dealing with complex systems and phenomena that need to be understood at the atomic level. This methodology combines the principles of both quantum mechanics and classical dynamics to simulate the behavior of molecular systems over time. In this document, we will discuss in depth the details of AIMD, its importance, and its applications in modern chemistry.

Introduction to molecular dynamics

Molecular dynamics (MD) simulations are computer simulations that track the motion of atoms and molecules over time. The basic idea is to solve Newton's equations of motion for a system of interacting particles. These simulations help chemists and physicists understand properties such as structure, dynamics, and thermodynamics of molecular assemblies.

In conventional MD simulations, the interactions between particles are defined by classical force fields. These force fields are empirical and estimate the potential energy surface (PES) of molecular systems based on experimental data or high-level quantum calculations. They are widely used due to their computational efficiency. Nevertheless, they lack the quantum mechanical accuracy needed to study reactions, excited states, and systems where electronic structures change.

What is ab initio molecular dynamics?

Ab initio, a Latin term meaning "from the beginning", denotes a technique in which no empirical parameters are used, instead focusing only on quantum mechanical calculations derived from first principles. Thus, AIMD methods calculate the forces acting on atoms and molecules using quantum mechanical calculations, rather than relying on pre-determined or fitted force fields.

AIMD can simulate the electronic structure of a system, allowing more accurate and detailed predictions of molecular behavior under different conditions. This approach is particularly useful for investigating chemical reactions, charge transfer, and other phenomena in molecular systems.

How does ab initio molecular dynamics work?

To understand AIMD it is important to understand the basic steps involved:

  1. Initialization:

    The simulation begins by defining the initial positions and velocities of all particles within the system. These can be random or derived from experimental or theoretical structures. For example, consider a simple water molecule, H 2 O in a box filled with other water molecules set up to simulate liquid water.

  2. Quantum mechanical calculations:

    At each time step of the simulation, quantum mechanical methods such as density functional theory (DFT) are used to calculate the electronic structure of the system. This provides a potential energy surface that affects the forces acting on the nuclei.

  3. Classical dynamics update:

    Newton's equations of motion are used to update the positions and velocities of the atoms based on the forces obtained from the potential energy surface.

    F = ma
  4. Repeat:

    This process is repeated at each subsequent time step, allowing the evolution of the system over time to be followed, and dynamics such as bond breaking and formation, and energy exchange to be captured.

Mathematical basis of AIMD

In AIMD, the forces acting on each atom are calculated using quantum mechanics. The dominant approach is density functional theory (DFT). DFT is used to calculate the forces acting on each atom as a derivative with respect to the total energy and the resulting atomic position.

The time evolution of the system can be described by integrating the forces from the DFT in time using the velocity Verlet algorithm. In a simplified equation it looks like this:

R(t + Δt) = R(t) + V(t)Δt + (1/2)F(t)/m(Δt)^2

where R(t) is the position, v(t) is the velocity, F(t) is the force, m is the mass, and Δt is the time step.

Advantages of ab initio molecular dynamics

AIMD offers several advantages over conventional MD:

  • Accuracy: Ab initio calculations allow for explicit consideration of electronic structures, and capture effects such as polarization, charge transfer, and other properties that are often missed by classical potentials.
  • Reactivity: Chemical reactions can be studied, as bond formation and breaking are naturally governed by quantum mechanical calculations.
  • Generality: Since AIMD does not rely on pre-defined force fields, it can be applied to a wide range of systems without the need for specific parameterization.

Limitations of AIMD

Despite its strengths, AIMD has some limitations:

  • Computation cost: The need for instantaneous quantum mechanical calculations remains resource-intensive, which limits the size and time scale of simulations compared to classical MD.
  • Time scale: Simulating long time scales (e.g., several nanoseconds) or very large systems is still impractical with current computational capabilities.

Applications of ab initio molecular dynamics

AIMD has wide applications in various areas of chemistry and materials science:

  • Biochemical mechanisms: Investigation of enzymatic reactions and protein-ligand interactions for drug discovery.
  • Materials science: Study of the electronic properties of materials, predicting the behavior of materials under different conditions.
  • Nanotechnology: Understanding nanoscale interactions that are highly dependent on the electronic structure.

Visual example of AIMD

Consider a scenario where one is studying the reaction mechanism for a simple reaction, such as the dissociation of molecular hydrogen, H 2 , into two hydrogen atoms, 2H. AIMD simulations can capture the electron density distribution and energy changes as bonds are broken and formed. Here is a conceptual illustration:

HHH 2

At any particular time step, the bond may be broken, resulting in this new example:

HH2H

Conclusion

Ab initio molecular dynamics bridges the gap between classical and quantum mechanics in the simulation of molecular systems. Despite its computational demand, it remains an indispensable tool for studying systems where electronic structure significantly affects molecular dynamics. As computational resources increase, the applicability and utility of AIMD is expected to expand, opening new avenues in the understanding of complex molecular phenomena.


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