Unified machine learning protocol for copolymer structure-property predictions.

Lei Tao, Tom Arbaugh, John Byrnes, Vikas Varshney, Ying Li

Abstract read

In one paragraph

Article in STAR protocols, 2022. 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.

The Reality of Active Targeting in Nanomedicine: Promise Versus Performance.Chembiochem : a European journal of chemical biology · 2026
Review

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

5 authors.

Lei TaoDepartment of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA.

Tom ArbaughDepartment of Physics, Wesleyan University, Middletown, CT 06459, USA.

John ByrnesSRI International, San Diego, CA 92131, USA.

Vikas VarshneyMaterials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH 45433, USA.

Ying LiDepartment of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA; Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1572, USA. Electronic address: yli2562@wisc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer types such as alternating, random, block, and gradient copolymers. We detail steps for necessary software installation and construction of datasets. We further describe training and optimization steps for four neural network models and subsequent model visualization and comparison using training and test values. For complete details on the use and execution of this protocol, please refer to Tao et al. (2022).

Indexed as

Machine LearningNeural Networks, ComputerPolymersSoftwarePolymersComputer sciencesMaterial sciences

Identifiers

PMID36595914

DOI10.1016/j.xpro.2022.101875

PMCPMC9700038

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

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.