Predicting material microstructure evolution via data-driven machine learning.

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.

1. Dislocation Substructures Evolution and an Informer Constitutive Model for a Ti-55511 Alloy in Two-Stages High-Temperature Forming with Variant Strain Rates in β Region.Materials (Basel, Switzerland) · 2023
   Article
2. Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation.Scientific reports · 2022
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

1 author.

Funding

No grant is acknowledged in the PubMed record.

Abstract