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Paper

Transferability of datasets between Machine-Learning Interaction Potentials

by Independent / Community 000a9872fa93aad5ad0bb29afa191ac3d266362a

Free2AITools Nexus Index

66.0

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 77

P: Popularity 52

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

With the emergence of Foundational Machine Learning Interatomic Potential (FMLIP) models trained on extensive datasets, transferring data between different ML architectures has become increasingly important. In this work, we examine the extent to which training data optimised for one machine-learning forcefield algorithm may be re-used to train different models, aiming to accelerate FMLIP fine-tuning and to reduce the need for costly iterative training. As a test case, we train models of an o...

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Semantic Scholar 12 Citations

Paper Information Summary
Entity Passport
Registry ID	000a9872fa93aad5ad0bb29afa191ac3d266362a
License	ArXiv
Provider	semantic_scholar

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Academic & Research Attribution

BibTeX

@misc{000a9872fa93aad5ad0bb29afa191ac3d266362a,
  author = {Unknown},
  title = {Transferability of datasets between Machine-Learning Interaction Potentials Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/000a9872fa93aad5ad0bb29afa191ac3d266362a}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Transferability of datasets between Machine-Learning Interaction Potentials [Paper]. Free2AITools. https://api.semanticscholar.org/000a9872fa93aad5ad0bb29afa191ac3d266362a

🔬Technical Deep Dive

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 77

Popularity (P) 52

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Transferability of datasets between Machine-Learning Interaction Potentials: Authority (A:77), Popularity (P:52), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"With the emergence of Foundational Machine Learning Interatomic Potential (FMLIP) models trained on extensive datasets, transferring data between different ML architectures has become increasingly important. In this work, we examine the extent to which training data optimised for one machine-learning forcefield algorithm may be re-used to train different models, aiming to accelerate FMLIP fine-tuning and to reduce the need for costly iterative training. As a test case, we train models of an o..."

❝ Cite Node

@article{Unknown2026Transferability,
  title={Transferability of datasets between Machine-Learning Interaction Potentials},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈12CitationsSemantic Scholar

🏛️77AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

📦Data Source: semantic_scholar

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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
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citations: 12

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