Automated Protocol-Driven Assessment of Scientific Literature for Risk Mitigation in Advanced Material Design: A HyperScore Framework

**Abstract:** This paper presents a framework, the Automated Protocol-Driven Assessment System (APDS), designed to systematically evaluate scientific literature related to advanced material design, specifically within the realm of layered transition metal dichalcogenides (LTMDs). APDS utilizes a multi-layered digital assessment pipeline incorporating semantic decomposition, logical consistency verification, replication feasibility scoring, and hyperdimensional knowledge graph analysis, culminating in a "HyperScore." This HyperScore provides a quantitative, dynamically adjusted metric representing the research's robustness, novelty, and potential impact, facilitating accelerated materials discovery and mitigating risks associated with pursuing flawed or irreproducible research directions. The framework aims to significantly reduce the time and resources dedicated to literature review and validation, enhance predictability of materials performance, and enable safer, more efficient designs.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-10
DOI
https://doi.org/10.5281/zenodo.16789662
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Automated Protocol-Driven Assessment of Scientific Literature for Risk Mitigation in Advanced Material Design: A HyperScore Framework

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Automated Protocol-Driven Assessment of Scientific Literature for Risk Mitigation in Advanced Material Design: A HyperScore Framework

article en

Abstract

**Abstract:** This paper presents a framework, the Automated Protocol-Driven Assessment System (APDS), designed to systematically evaluate scientific literature related to advanced material design, specifically within the realm of layered transition metal dichalcogenides (LTMDs). APDS utilizes a multi-layered digital assessment pipeline incorporating semantic decomposition, logical consistency verification, replication feasibility scoring, and hyperdimensional knowledge graph analysis, culminating in a "HyperScore." This HyperScore provides a quantitative, dynamically adjusted metric representing the research's robustness, novelty, and potential impact, facilitating accelerated materials discovery and mitigating risks associated with pursuing flawed or irreproducible research directions. The framework aims to significantly reduce the time and resources dedicated to literature review and validation, enhance predictability of materials performance, and enable safer, more efficient designs.

Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Openalex Percentile: Top 65%
Machine Learning in Materials Science
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Automated Protocol-Driven Assessment of Scientific Literature for Risk Mitigation in Advanced Material Design: A HyperScore Framework · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS