ReviewMaterials (Basel, Switzerland)2021
Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges.
Review in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Artificial intelligence-driven magnetic property prediction and materials discovery for next-generation spintronics.RSC advances · 2026Review
- Mechanochemical and machine-intelligent design of programmable materials: From molecular interactions to macroscale functionality.Materials today. Bio · 2026Review
- Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain.Scientific reports · 2025Article
- Unleashing the Power of Artificial Intelligence in Materials Design.Materials (Basel, Switzerland) · 2023Review
- Machine learning dislocation density correlations and solute effects in Mg-based alloys.Scientific reports · 2023Article
- Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions.ACS omega · 2022Article
- Machine learning potential for interacting dislocations in the presence of free surfaces.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials' discovery or develop new understanding of materials' behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.
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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.