This Special Issue highlights three complementary themes:
Learning the ingredients of electronic-structure calculations: Contributions predicting electron densities, Hamiltonian elements, or other effective one- and two-body operators from atomic configurations, while exploiting symmetries and uncertainty quantification, are encouraged.
ML-accelerated many-body methods: Studies embedding ML models into wave-function theories—from coupled-cluster and multireference to variational and diffusion Monte Carlo—aiming to reduce scaling, extend reach, or improve accuracy.
Data-driven density functionals: Manuscripts developing and testing exchange–correlation functionals, kinetic-energy functionals, or orbital-free models with supervised, unsupervised, or reinforcement learning are welcome.
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