ReviewPatterns (New York, N.Y.)2025
Generating and leveraging explanations of AI/ML models in materials and manufacturing research.
Review in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Prompt Engineering Accelerates the Data-Driven Discovery of Photocatalysts via an LLM-Based Model Ensemble Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In some technical domains, machine learning (ML) tools, typically used with large datasets, must be adapted to small datasets, opaque design spaces, and expensive data generation. Specifically, generating data in many materials or manufacturing contexts can be expensive in time, materials, and expertise. Additionally, the "thought process" of complex "black box" ML models is often obscure to key stakeholders. This limitation can result in inefficient or dangerous predictions when errors in data processing or model training go unnoticed. Methods of generating human-interpretable explanations of complex models, called explainable artificial intelligence (XAI), can provide the insight needed to prevent these problems. In this review, we briefly present XAI methods and outline how XAI can also inform future behavior. These examples illustrate how XAI can improve manufacturing output, physical understanding, and feature engineering. We present guidance on using XAI in materials science and manufacturing research with the aid of demonstrative examples from literature.
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.