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Paper

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

by Independent / Community 0010848c4044122dd79c12e3612091ac545a6e88

Free2AITools Nexus Index

72.3

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 91

P: Popularity 70

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a...

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Semantic Scholar 581 Citations

Paper Information Summary
Entity Passport
Registry ID	0010848c4044122dd79c12e3612091ac545a6e88
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{0010848c4044122dd79c12e3612091ac545a6e88,
  author = {Unknown},
  title = {Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0010848c4044122dd79c12e3612091ac545a6e88}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning [Paper]. Free2AITools. https://api.semanticscholar.org/0010848c4044122dd79c12e3612091ac545a6e88

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 91

Popularity (P) 70

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning: Authority (A:91), Popularity (P:70), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

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📝 Executive Summary

"Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a..."

❝ Cite Node

@article{Unknown2026Approaching,
  title={Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈581CitationsSemantic Scholar

🏛️91AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️vision multimediaField

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🆔 Identity & Source

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

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 581

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