Nonlocal machine-learned exchange functional for molecules and solids

Here, the authors use machine learning and exact physical constraints to design a nonlocal exchange functional for both molecular and solid-state systems. The model is computationally efficient, achieves hybrid-DFT accuracy on molecular benchmarks, and improves the accuracy of band gap predictions over semilocal DFT. To demonstrate the efficiency and accuracy of the model, the authors compute charged point defect transition levels in silicon in good agreement with experiment, a task previously only possible with more expensive hybrid DFT calculations.