The 2024 Nobel Prizes for physics and chemistry were announced on 8 and 9 October respectively. Both prizes were awarded for work enabling or using machine learning. More specifically, Hopfield is recognised for "inventing a network that uses a method for saving and recreating patterns". This Hopfield network utilises physics that describes a material's characteristics due to its atomic spin. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy.

Today, AI is used in almost every industry, and tools provided by artificial intelligence in science are no exception. The amount of data generated by many of today's physics and astronomy studies is so great that no human or group of humans could keep up. Some of them daily record gigabytes of data, and the torrent is just getting bigger. Many scientists are looking to artificial intelligence for assistance due to the flood. Artificial neural networks, which are computer-simulated neurons replicating the function of brains, can plow through mounds of data with little to no human input, emphasizing abnormalities and seeing patterns that people would never have noticed.

Physicists prove that classical machine learning models can improve predictions about quantum materials.

Many real-world problems involve datasets where only some of the data is labeled and the rest is unlabeled. In this post, we discuss our implementation of semi-supervised learning for predicting the synthesizability of theoretical materials. When we think about the materials that will enable next-generation technologies, it's probably not the case that there is one ultimate material waiting to be found that will solve all our problems. The problems we need to solve (producing and storing clean energy, mitigating climate change, desalinating water, etc.) are complex and varied. Even zooming in to the next-generation of electronics, computers, and nanotechnology, there probably isn't a single perfect material to exploit in the same way that silicon has been used in all our familiar devices.