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Benchmarking electronic structure methods using generative AIto construct diverse reaction databases
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DOI：10.1016/j.cplett.2026.142765.png)
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Abstract
En 中文
Highly diverse chemical databases play a key role in the development of accurate and generally applicable electronic structure methods. We develop and test a workflow for autonomously generating reaction databases using large language models. As a proof-of-concept, we generate a reaction database encompassing Diels-Alder cycloaddition, electrocyclic ring opening, electrophilic addition, electrophilic aromatic substitution, and acid-base reactions. We proceed to benchmark DFT methods relative to CCSD(T) reaction energies obtained via G4 (MP2) theory. The best-performing DFT methods from each of the rungs of Jacob's Ladder (mean-absolute-deviations in parentheses) are: PBE-D4 (1.91), Skala (1.86), CAM-B3LYP-D4 and omega B97X-D (1.49), and B1B95-D4 (1.48 kcal mol(-1)).
Keywords:
Large language models
Density functional theory
Chemical databases
DFT benchmarking
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