What is a Phase Transition?
Popularized in physical and biological science, phase transition originally referred to the transition between an element's states (gas, liquid, solid), however the term has expanded to describe any abrupt and sudden change in one of the order parameters describing an arbitrary system, when a control parameter approaches a critical value. In short, a phase transition is the point at which an element or object changes, usually as a result of outside or external factors.
How does a Phase Transition work?
Often using temperature as the catalyst for change, phase transitions measure the external factors surrounding an element as it changes or transitions. For example, the transition of water from a liquid to gas form is as a result of a rising temperature of both the water, and its environment. During its transition from liquid to gas, the water reaches its boiling point, also known as the critical point, and abruptly changes its volume as it expands. The measurement of the change of a specific characteristic of an object is called the order parameter. For example, the measured difference in densities between a liquid and a gas is the order parameter.
Order Transitions
There are two main forms of phase transitions, first and second order. A first-order phase transition is one that involved latent heat. This means that during the phase transition, there is a transfer of heat, either exothermal or endothermic. A second-order phase transition is also called a "continuous phase transition." Second-order phase transitions typically take place over an extended amount of time, and happen smoothly without abrupt changes to an object's thermodynamics. An example of a second-order phase transition is a ferromagnetic transition, where an object transitions to become magnetically charged.
Phase Transitions and Machine Learning
Machine learning algorithms have been used in helping scientists
better understand phase transitions. By training a neural network to define specific phase transition boundaries, the system is able to predict possible critical point values, represented by temperature. Algorithms for predicting and exploring critical points in phase transition have potential to be applied toward unexplored statistical models as well.
