Algorithm out of Google’s DeepMind finesses DFT calculations

A neural network constructed by researchers at Google’s artificial intelligence arm DeepMind has produced a refined density functional theory algorithm. The algorithm, which considers ‘fractional electronic character’ overlooked in previous DFT algorithms, is able to avoid some of the pitfalls that often lead these algorithms to give highly inaccurate results.

The Schrödinger equation cannot be solved analytically for any atomic system more complex than the hydrogen atom because electron–electron repulsion means the variables involved increase dramatically. This means that to create realistic approximations of molecular systems, researchers have to turn to computational methods. For some small molecules, it is feasible to find approximate numerical solutions to the equation for each electron and estimate the shape of each molecular orbital from the resulting wavefunctions. However, the computational demands are huge and grow steeply, as every additional electron repels every other. ‘The question is how do you go beyond this area?’ says DeepMind’s Aron Cohen.