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This is a Quantitative Study study.

Automatic Molecule Fragmentation for Density Matrix Embedding Theory

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Graph-based automatic fragmentation improves accuracy and reduces computational costs in quantum chemistry using density matrix embedding theory.

GAF-DMET achieves comparable or higher accuracy than ABE while reducing wall-clock time.
Graph-based fragmentation patterns are determined by solving a graph partitioning problem efficiently.
Evaluation of 14 small molecules validates the effectiveness of GAF in selecting accurate fragmentation patterns.
GAF-DMET outperforms ABE in key computational tasks, such as binding energy calculations and SN2 reactions.

Imamura et al. (2025) studied this question.

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View Full Paper

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Authors

Graph-based automatic fragmentation improves accuracy and reduces computational costs in quantum chemistry using density matrix embedding theory.

GAF-DMET achieves comparable or higher accuracy than ABE while reducing wall-clock time.
Graph-based fragmentation patterns are determined by solving a graph partitioning problem efficiently.
Evaluation of 14 small molecules validates the effectiveness of GAF in selecting accurate fragmentation patterns.
GAF-DMET outperforms ABE in key computational tasks, such as binding energy calculations and SN2 reactions.

Imamura et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark