MLCI with the ANN used as a hash function, configuration state functions, and geometry-to-geometry wavefunction transfer gives near-FCI potential energy curves for N2 and CO more cheaply than stochastic Monte Carlo CI.
O.; Rupp, M.; von Lilienfeld, O
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Machine learning configuration interaction for ab initio potential energy curves
MLCI with the ANN used as a hash function, configuration state functions, and geometry-to-geometry wavefunction transfer gives near-FCI potential energy curves for N2 and CO more cheaply than stochastic Monte Carlo CI.