The foundations for understanding the electronic structure of many-electron atoms are introduced. We start from the discovery of the spin and introduce spin operators. The spin existence is shown to “upgrade” the state of single particles into a product space with the spin subspace, and to impose constraints on states of identical particles, which must be symmetric (bosons) or antisymmetric (fermions) under particle transpositions. The many-electron state in the atom is therefore approximated as an antisymmetrized products (Slater determinant) of single-electron states (spin-orbitals). The variationally optimal orbitals are shown to be solutions to the Hartree–Fock equations, and the assignment of electrons to these orbitals in the atomic ground state reflects the Pauli exclusion and Aufbau principles, thus explaining the trends in the periodic table of the elements in terms of their electronic configurations. Special attention is given to two-electron systems, demonstrating the exchange stabilization of triplet versus singlet states (Hund’s rule).

Exact solutions for infinite Ising systems are rare, specific in terms of the interactions allowed, and limited to one and two dimensions. To study a wider range of models we must resort to various approximation techniques. One of the simplest and most comprehensive of these is the mean-field approximation, the subject of this chapter. Some versions of this approximation rely on a self-consistent requirement, and in this respect the mean-field method for the Ising model is similar to a number of other self-consistent approximation methods in physics, including the Hartree–Fock approximation for atomic and molecular orbitals, the BCS theory of superconductivity, and the relaxation method for determining electric potentials. We will also introduce a somewhat different mean-field approach, the Landau–Ginzburg approximation, which is based on a series expansion of the free energy. One of the drawbacks of all of the mean-field theories, however, is that they predict the same mean-field critical exponents, which, unfortunately, are at odds with the results of exact solutions and experiments.

A useful way to solve a complex problem – whether in physics, mathematics, or life in general – is to break it down into smaller pieces that can be handled more easily. This is especially true of the Ising model. In this chapter, we investigate various partial-summation techniques in which a subset of Ising spins is summed over to produce new, effective couplings among the remaining spins. These methods are useful in their own right and are even more important when used as a part of position-space renormalization-group techniques.

In the chapters so far, we have studied a number of exact methods of calculation for Ising models. These studies culminated in the exact solution for an infinite one-dimensional Ising model, as well as the corresponding solution on a 2 × ∞ lattice. Neither of these systems shows a phase transition, however. In this chapter, we start with Onsager’s exact solution for the two-dimensional lattice, which quite famously does have a phase transition. Next, we explore exact series expansions from low and high temperature, and show how these results can be combined, via the concept of duality, to give the exact location of the phase transition in two dimensions.

In Chapter 3 we explored transformations where a finite group of Ising spins is summed to produce effective interactions among the remaining spins. In all of these cases a finite sum of Boltzmann factors is sufficient to solve the problem. We turn now to infinite systems, where a straightforward, brute-force summation is not possible. Instead, we develop a number of new techniques that allow us to evaluate an infinite summation in full detail.

Kenneth Wilson introduced the renormalization-group (RG) approach in 1971. This approach gave new life to the study of the Ising model. The implications of this breakthrough were immediately recognized by researchers in the field, and Wilson and the RG technique were awarded the Nobel Prize in Physics soon thereafter. One of the distinguishing features of RG methods is that they explicitly include the effects of fluctuations. In addition, the RG approach gives a natural understanding of the universality that is seen in critical phenomena in general, and in critical exponents in particular. In many respects, the RG approach gives a deeper understanding not only of the Ising model itself, but of all aspects of critical phenomena. The original version of the renormalization-group method was implemented in momentum space – which is a bit like studying a system with Fourier transforms. It is beyond the scope of this presentation. Following that, various investigators extended the approach to position space, which is more intuitive in many ways and is certainly much easier to visualize. We present the basics of position-space renormalization group methods in this chapter. We will also explain the origin of the terms “renormalization” and “group” in the RG part of the name.

In this chapter, we explore Ising systems that consist of just one or a few spins. We define a Hamiltonian for each system and then carry out straightforward summations over all the spin states to obtain the partition function. No phase transitions occur in these systems – in fact, an infinite system is needed to produce the singularities that characterize phase transitions. Even so, our study of finite systems yields a number of results and insights that are important to the study of infinite systems.

Few models in theoretical physics have been studied for as long, or in as much detail, as the Ising model. It’s the simplest model to display a nontrivial phase transition, and as such it plays a unique role in theoretical physics. In addition, the Ising model can be applied to a wide range of physical systems, from magnets and binary liquid mixtures, to adsorbed monolayers and superfluids, to name just a few. In this chapter, we present some of the background material that sets the stage for a detailed study of the Ising model in the chapters to come.