Evidence map›Paper›PMID 37730824›Full record

ArticleNature communications2023

Capturing dynamical correlations using implicit neural representations.

Sathya R Chitturi, Zhurun Ji, Alexander N Petsch, Cheng Peng, Zhantao Chen, Rajan Plumley, Mike Dunne, Sougata Mardanya, Sugata Chowdhury, Hongwei Chen and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

19 authors.

Sathya R ChitturiSLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. chitturi@stanford.edu.ORCID http://orcid.org/0000-0002-0298-8476
Zhurun JiDepartment of Physics and Applied Physics, Stanford University, Stanford, CA, 94305, USA. zhurun@stanford.edu.ORCID http://orcid.org/0000-0002-0751-275X
Alexander N PetschSLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. apetsch@stanford.edu.ORCID http://orcid.org/0000-0002-7597-9626
Cheng PengStanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA.
Zhantao ChenStanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA.
Rajan PlumleySLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
Mike DunneSLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
Sougata MardanyaDepartment of Physics and Astrophysics, Howard University, Washington, DC, USA.ORCID http://orcid.org/0000-0002-5821-9908
Sugata ChowdhuryDepartment of Physics and Astrophysics, Howard University, Washington, DC, USA.
Hongwei ChenDepartment of Physics, Northeastern University, Boston, USA.ORCID http://orcid.org/0000-0003-2264-9989
Arun BansilDepartment of Physics, Northeastern University, Boston, USA.ORCID http://orcid.org/0000-0002-3864-3461
Adrian FeiguinDepartment of Physics, Northeastern University, Boston, USA.ORCID http://orcid.org/0000-0001-5509-2561
Alexander I KolesnikovNeutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.ORCID http://orcid.org/0000-0003-1940-4649
Dharmalingam PrabhakaranDepartment of Physics, University of Oxford, Clarendon Laboratory, Oxford, OX1 3PU, UK.ORCID http://orcid.org/0000-0002-7769-9716
Stephen M HaydenH.H. Wills Physics Laboratory, University of Bristol, Bristol, BS8 1TL, UK.ORCID http://orcid.org/0000-0002-3209-027X
Daniel RatnerSLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
Chunjing JiaSLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.ORCID http://orcid.org/0000-0001-7999-1932
Youssef NashedSLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
Joshua J TurnerSLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. joshuat@slac.stanford.edu.ORCID http://orcid.org/0000-0002-2106-7955

Funding

DOE | SC | Basic Energy Sciences (BES) DE-AC02-76SF0051RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/L015544/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/R011141/1
6 · The paper itself

Abstract

Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages 'neural implicit representations' that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La

Identifiers

PMID37730824
PMCPMC10511537

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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.