Statistical Mechanics with worked examples

In summary, the conversation discusses the frustration of a graduate student in a Stat Mech course due to the lack of practical examples and connections to real-world problems. The student has found that despite researching various textbooks, they all seem to focus on definitions rather than applications. However, there are examples where ensemble theory can be used to make explicit calculations, such as calculating equilibrium constants and thermodynamics of different types of gases. Additionally, the conversation touches on the use of classical stat mech in computational biophysics, which involves techniques like molecular dynamics and Monte Carlo simulations. Overall, the formalism of classical stat mech is valuable for trustworthy calculations, but the practical applications may differ.
  • #1
wotanub
230
8
I'm taking Stat Mech at the graduate level and I find the course incredibly frustrating in that it just feels like a whole lot of mathematical formalism without any examples. I've looked in Paritha, Huang, and Salinas and all these books seem the same in that they just looks like a bunch of definitions for the various ensembles, and it's not clear how any of it connects to the problems. Ensemble theory just seems pointless since I've never seen a treatment that demonstrates how it can be used to make calculations.
 
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  • #2
Here are some examples:
1) In some cases the partition function can be used to explicitly calculate a chemical reactions equilibrium constant.
2) Einstein and Debye Theory of crystals are statistical in nature.
3) Phonons
4) The thermodynamics of an ideal monotomic and diatomic can be explicitly calculated.
5) The thermodynamics of a polyatomic can be treated well.
6) The Theory of imperfect gases/Kinetic Theory of gases.
7) Classical liquid theory (radial distribution functions) and perturbation theory.
8) Phase transitions (renormalization group!)
9) Conduction of electrons in metals.


10) Computational biophysics uses classical stat mech. Molecular dynamics, monte carlo simulations, etc. The formalism of classical stat mech allows us to trust our calculations (reasonably so) but admittedly the practical calculations are very different.
 

1. What is Statistical Mechanics?

Statistical Mechanics is a branch of physics that uses statistical methods to explain and predict the behavior of large systems of particles, such as atoms and molecules. It helps us understand how macroscopic properties, like temperature and pressure, emerge from the microscopic interactions between individual particles.

2. How does Statistical Mechanics work?

Statistical Mechanics uses mathematical tools and concepts from probability theory, thermodynamics, and quantum mechanics to analyze the behavior of large systems of particles. It uses statistical ensembles, which are collections of possible states that a system can be in, to calculate the probabilities of different outcomes and predict the overall behavior of the system.

3. What are some examples of Statistical Mechanics in action?

Statistical Mechanics can be applied to many different systems, from gases and liquids to solids and plasmas. It can explain phenomena such as phase transitions, diffusion, and thermal equilibrium. It is also used in fields such as chemistry, biology, and materials science to understand the behavior of complex systems.

4. How are statistical methods used in Statistical Mechanics?

Statistical methods, such as probability distributions and statistical averages, are used in Statistical Mechanics to calculate the behavior of large systems based on the properties of individual particles. These methods help us understand how the macroscopic properties of a system emerge from the microscopic interactions between particles.

5. What are some practical applications of Statistical Mechanics?

Statistical Mechanics has many practical applications in fields such as engineering, materials science, and biology. It is used to design new materials with specific properties, understand and predict chemical reactions, and model biological systems. It also has applications in fields such as economics and social sciences, where it can be used to analyze complex systems and make predictions about their behavior.

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