A Transformer-based learning-to-rank model for selected configuration interaction achieves chemical accuracy with substantially fewer determinants than prior classification or regression baselines across tested molecules.
Journal of Chemical Theory and Computation , volume=
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A perturbative CCSD trial wavefunction renders AFQMC size-extensive with negligible bias, matching CISD-level accuracy on small systems while avoiding infrared divergence in the uniform electron gas thermodynamic limit unlike CCSD(T).
citing papers explorer
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Learning to Rank for Selected Configuration Interaction
A Transformer-based learning-to-rank model for selected configuration interaction achieves chemical accuracy with substantially fewer determinants than prior classification or regression baselines across tested molecules.
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Size Extensive Auxiliary-Field Quantum Monte Carlo with Perturbative Coupled Cluster Trial Wavefunction
A perturbative CCSD trial wavefunction renders AFQMC size-extensive with negligible bias, matching CISD-level accuracy on small systems while avoiding infrared divergence in the uniform electron gas thermodynamic limit unlike CCSD(T).