Evidence map›Paper›PMID 35177785›Full record

ArticleNature materials2022

Correlative image learning of chemo-mechanics in phase-transforming solids.

Haitao D Deng, Hongbo Zhao, Norman Jin, Lauren Hughes, Benjamin H Savitzky, Colin Ophus, Dimitrios Fraggedakis, András Borbély, Young-Sang Yu, Eder G Lomeli and 7 more

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Nature materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
11.1field-weighted citation impact, top 2% of its field
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

9 citing papers in PubMed, 57 citations in OpenAlex.

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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

17 authors at 5 institutions in 3 countries.

Haitao D DengDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-3225-4219
Hongbo ZhaoDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-0932-0719
Norman JinDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
Lauren HughesNational Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Benjamin H SavitzkyNational Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Colin OphusNational Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0003-2348-8558
Dimitrios FraggedakisDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-3301-6255
András BorbélyCentre SMS, Georges Friedel Laboratory (UMR 5307), Mines Saint-Etienne, Univ. Lyon, CNRS, Saint-Etienne, France.
Young-Sang YuAdvanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-0545-9019
Eder G LomeliDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
Rui YanInstitute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
Jueyi LiuInstitute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
David A ShapiroAdvanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-4186-6017
Wei CaiDepartment of Mechanical Engineering, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-5919-8734
Martin Z BazantDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-8200-4501
Andrew M MinorNational Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. aminor@berkeley.edu.ORCID http://orcid.org/0000-0003-3606-8309
William C ChuehDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA. wchueh@stanford.edu.ORCID http://orcid.org/0000-0002-7066-3470
Lawrence Berkeley National Laboratory · USStanford University · USMassachusetts Institute of Technology · USSLAC National Accelerator Laboratory · USUniversité Claude Bernard Lyon 1 · FR

Funding

DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab) DE-AC02-05CH11231DOE | SC | Basic Energy Sciences (BES) DE-AC02-05CH11231DOE | SC | Basic Energy Sciences (BES) DE-AC02-76SF00515DOE | SC | Basic Energy Sciences (BES) DE-SC0010412
6 · The paper itself

Abstract

Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (for example, due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. Here we developed a generalizable, physically constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on Li

Identifiers

PMID35177785
OpenAlexW3180225019

What OpenQuestion holds

Textmetadata
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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.