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Paper

Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties.

by Independent / Community 0031585d29eaef151d147db6402c2a4812a4d832

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69.5

S: Semantic 50

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A: Authority 85

P: Popularity 62

R: Recency 100

Q: Quality 65

Tech Context

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Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. T...

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Paper Information Summary
Entity Passport
Registry ID	0031585d29eaef151d147db6402c2a4812a4d832
License	ArXiv
Provider	semantic_scholar

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BibTeX

@misc{0031585d29eaef151d147db6402c2a4812a4d832,
  author = {Unknown},
  title = {Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties. Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0031585d29eaef151d147db6402c2a4812a4d832}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties. [Paper]. Free2AITools. https://api.semanticscholar.org/0031585d29eaef151d147db6402c2a4812a4d832

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⚖️ Free2AITools Nexus Index V2.0

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 85

Popularity (P) 62

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties.: Authority (A:85), Popularity (P:62), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. T..."

❝ Cite Node

@article{Unknown2026Efficient,
  title={Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties.},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈72CitationsSemantic Scholar

🏛️85AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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🏷️ Research Topics

vector databases

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

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params billions: null
context length: null
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citations: 72

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