Journal ArticleArtificial intelligence “sees” split electronsPerdew, John P.Chemical bonds between atoms are stabilized by the exchange-correlation (xc) energy, a quantum-mechanical effect in which “social distancing” by electrons lowers their electrostatic repulsion energy. Kohn-Sham density functional theory (DFT) ( 1 ) states that the electron density determines this xc energy, but the density functional must be approximated. This is usually done by satisfying exact constraints of the exact functional (making the approximation predictive), by fitting to data (making it interpolative), or both. Two exact constraints—the ensemble-based piecewise linear variation of the total energy with respect to fractional electron number ( 2 ) and fractional electron z -component of spin ( 3 )—require hard-to-control nonlocality. On page 1385 of this issue, Kirkpatrick et al. ( 4 ) have taken a big step toward more accurate predictions for chemistry through the machine learning of molecular data plus the fractional charge and spin constraints, expressed as data that a machine can learn.2021-12-1010326295Science37465731322 to 13230036-8075https://doi.org/10.1126/science.abm24451939528National Science Foundation