Computational Chemistry and Thermodynamics

American Chemical Society event in December. The speaker graduated from Stuyvesant in 2019, attended Brandeis, and did research and an internship at Oak Ridge National Laboratory. Currently a second-year graduate student at Yale, discussing computational chemistry principles and goals, such as understanding thermodynamic quantities.

Numerical Modeling

Some chemistry problems are complex; simplifications help create solvable contexts. Two methods:

1. Numerical: Iterative solution.
2. Analytical: Solve for x.
   Example: Use Euler's method to define reaction rates. Concentration curves can be generated without lab measurements. Adding steps can increase complexity. Intermediate concentrations aren't commonly discussed, but they impact outcomes.

Statistical Mechanics

Chemistry often lacks deterministic solutions. Statistical mechanics describes systems where particles behave randomly. Probability distributions determine particle behavior, as seen in random walk models. For systems like spins in magnetic fields, the Boltzmann distribution offers insight into state probabilities.

Ising Model

In a 2D grid, neighboring spins interact. Monte Carlo simulations show correlations at low temperatures, with magnetization lost above 2.3 Kelvin.

Quantum Chemistry

Schrodinger's equation is complex for multi-electron systems; alternative methods yield valuable insights. Focus on solubility rules and factors influencing dissociation energies.