Science is often perceived as a reductionist discipline which seeks to establish the basic laws of nature and derive all other observations from these basic laws. In practice there is often a barrier of computational complexity separating our understanding of matter on a small scale from our ability to make predictions on a larger scale.
Indeed, protons emerge from quarks, biology stems from chemistry, and intelligence, arguably, from coupled neurons. However, understanding how such complex structures emerge from simple rules is clearly a difficult and fascinating task. Such emergence is central to condensed matter physics where we study the complex phenomena generated by electrons in various materials.
Topological phases of matter
The interplay of quantum mechanics, topology, and material design is leading to the unravel of new exotic phases of matter known as topological phases.
Deep learning and physics
Can insights from physics improve deep learning algorithms ? Is there a thermodynamical description of deep learning ? Which branches of physics can be automated using deep learning ?
Complexity aspects of physics
Which many-body physical phenomena can be simulated effectively using a standard computer ? Using today's state of the art quantum computers, can we tackle new problems in many-body physics ?
A way of automating the search for relevant degrees of freedom using information theory and neural networks.
An exotic physical effect, related to quantized heat transport, is shown to be an obstacle to quantum Monte Carlo simulations.
Quasicrystals can exhibit topological phases of matter which occur only in four dimensional crystals.