Evidence map›Paper›PMID 41767480›Full record

ArticleMacromolecules2026

Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films.

Bradley Lamb, Saroj Upreti, Yunfei Wang, Daniel Struble, Chenhui Zhu, Guillaume Freychet, Xiaodan Gu, Boran Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Macromolecules, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

5 · Who and what money

Authors and funding

8 authors.

Bradley LambSchool of Polymer Science and Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, Mississippi 39406, United States.
Saroj UpretiSchool of Polymer Science and Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, Mississippi 39406, United States.
Yunfei WangSchool of Polymer Science and Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, Mississippi 39406, United States.ORCID https://orcid.org/0000-0001-7555-5308
Daniel StrubleSchool of Polymer Science and Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, Mississippi 39406, United States.
Chenhui ZhuAdvanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.ORCID https://orcid.org/0000-0003-1263-5065
Guillaume FreychetNSLS-II, Brookhaven National Laboratory, Upton, New York 11973, United States.
Xiaodan GuSchool of Polymer Science and Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, Mississippi 39406, United States.ORCID https://orcid.org/0000-0002-1123-3673
Boran MaSchool of Polymer Science and Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, Mississippi 39406, United States.ORCID https://orcid.org/0000-0002-5172-1196

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (

Identifiers

PMID41767480
PMCPMC12854768

What OpenQuestion holds

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Registered trials

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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.