ReviewiScience2026
AI empowerment: Establishing the synergy between artificial intelligence and electromagnetic shielding materials research.
Review in iScience, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Transforming Nanomaterials Development with Artificial Intelligence Techniques.Nanotechnology, science and applications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing intelligence of electronic information systems, urgent demand exists for high-performance customized electromagnetic shielding materials. However, traditional research and development paradigms are constrained by critical bottlenecks, like multi-parameter coupling, high trial-and-error costs, and multi-scale design hurdles, failing to satisfy efficient material development needs. Artificial intelligence (AI), leveraging its core advantages of data-driven approaches and algorithmic optimization, offers a transformative paradigm to overcome these bottlenecks. Although AI-driven methodologies have demonstrated tremendous potential in the design of electromagnetic shielding materials, systematic barriers from inadequate interdisciplinary collaboration still limit its technological empowerment. In response to these challenges, we highlight the following solutions to advance AI-enabled electromagnetic shielding material design: (1) developing neural network models driven by data-physics integration, (2) developing domain-specific large language models for electromagnetic shielding materials, (3) establishing comprehensive databases for electromagnetic shielding materials, and (4) promoting domain-specific data sharing and the construction of standardized protocols.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.