In September, Alphabet’s DeepMind published a paper in the journal Physical Review Research detailing Fermionic Neural Network (FermiNet), a new neural network architecture that’s well-suited to modeling the quantum state of large collections of electrons. The FermiNet, which DeepMind claims is one of the first demonstrations of AI for computing atomic energy, is now available in open source on GitHub — and ostensibly remains one of the most accurate methods to date.
In quantum systems, particles like electrons don’t have exact locations. Their positions are instead described by a probability cloud. Representing the state of a quantum system is challenging, because probabilities have to be assigned to possible configurations of electron positions. These are encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons; the wavefunction squared gives the probability of finding the system in that configuration.
The space of possible configurations is enormous — represented as a grid with 100 points along each dimension, the number of electron configurations for the silicon atom would be larger than the number of atoms in the universe. Researchers at DeepMind believed that AI could help in this regard. They surmised that, given neural networks have historically fit high-dimensional functions in artificial intelligence problems, they could be used to represent quantum wavefunctions as well.
By way of refresher, neural networks contain neurons (mathematical functions) arranged in layers that transmit signals from input data and slowly adjust the synaptic strength — i.e., weights — of each connection. That’s how they extract features and learn to make predictions.
Because electrons are a type of particle known as fermions, which include the building blocks of most matter (e.g., protons, neutrons, quarks, and neutrinos), their wavefunction has to