ArticleNature communications2026
Solving the vibrational Schrödinger equation with artificial neural networks.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Evaluating Variational Quantum Eigensolver Approaches for Simplified Models of Molecular Systems: A Case Study on Protocatechuic Acid.Molecules (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
Artificial neural networks are universal function approximators and have shown great ability in computing the ground-state energy of the electronic Schrödinger equation, yet have not established themselves as a practical and accurate approach for solving the vibrational Schrödinger equation for realistic polyatomic molecules. Here, we propose an efficient neural-network approach for solving the vibrational Schrödinger equation and provide a detailed illustration using the methane molecule. To demonstrate the power of the proposed method, we then apply it to propane, an 11-atom molecule with 27 vibrational degrees of freedom. Using a neural network with fewer than 15,000 parameters, we obtain the ground-state energy within 1 cm
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Registered trials
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