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; Arun Bansil; Adrian Feiguin; Alexander I. Kolesnikov; Dharmalingam Prabhakaran; Stephen M. Hayden; Daniel Ratner; Chunjing Jia; Youssef Nashed; Joshua J. Turner

doi:10.1038/s41467-023-41378-4

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, Arun Bansil, Adrian Feiguin, Alexander I. Kolesnikov, Dharmalingam Prabhakaran, Stephen M. Hayden, Daniel Ratner, Chunjing Jia, Youssef Nashed, Joshua J. Turner

Direct Geometry

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

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 La₂NiO₄, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

Original language	English
Article number	5852
Journal	Nature Communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-41378-4
State	Published - Dec 2023

Funding

This work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0022216, as well as under Contract DE-AC02-76SF00515, both for the Materials Sciences and Engineering Division, as well as for the Linac Coherent Light Source (LCLS), part of the Scientific User Facilities Division. A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. J.J. Turner acknowledges support from the U.S. DOE, Office of Science, Basic Energy Sciences through the Early Career Research Program. Z.J. is supported by the Stanford Science fellowship, and the Urbanek -Chodorow postdoctoral fellowship awards. A.N. Petsch and S.M. Hayden acknowledge funding and support from the Engineering and Physical Sciences Research Council (EPSRC) under Grant Nos. EP/L015544/1 and EP/R011141/1. This work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0022216, as well as under Contract DE-AC02-76SF00515, both for the Materials Sciences and Engineering Division, as well as for the Linac Coherent Light Source (LCLS), part of the Scientific User Facilities Division. A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. J.J. Turner acknowledges support from the U.S. DOE, Office of Science, Basic Energy Sciences through the Early Career Research Program. Z.J. is supported by the Stanford Science fellowship, and the Urbanek -Chodorow postdoctoral fellowship awards. A.N. Petsch and S.M. Hayden acknowledge funding and support from the Engineering and Physical Sciences Research Council (EPSRC) under Grant Nos. EP/L015544/1 and EP/R011141/1.

Funders	Funder number
Stanford Science
U.S. Department of Energy
Office of Science
Basic Energy Sciences	DE-SC0022216, DE-AC02-76SF00515
Oak Ridge National Laboratory
Engineering and Physical Sciences Research Council	EP/R011141/1, EP/L015544/1

Access to Document

10.1038/s41467-023-41378-4

Cite this

Chitturi, S. R., Ji, Z., Petsch, A. N., Peng, C., Chen, Z., Plumley, R., Dunne, M., Mardanya, S., Chowdhury, S., Chen, H., Bansil, A., Feiguin, A., Kolesnikov, A. I., Prabhakaran, D., Hayden, S. M., Ratner, D., Jia, C., Nashed, Y., & Turner, J. J. (2023). Capturing dynamical correlations using implicit neural representations. Nature Communications, 14(1), Article 5852. https://doi.org/10.1038/s41467-023-41378-4

@article{6594edd3d07046eeb9c945fc3a1d21b6,

title = "Capturing dynamical correlations using implicit neural representations",

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 {\textquoteleft}neural implicit representations{\textquoteright} 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 La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.",

author = "Chitturi, {Sathya R.} and Zhurun Ji and Petsch, {Alexander N.} and Cheng Peng and Zhantao Chen and Rajan Plumley and Mike Dunne and Sougata Mardanya and Sugata Chowdhury and Hongwei Chen and Arun Bansil and Adrian Feiguin and Kolesnikov, {Alexander I.} and Dharmalingam Prabhakaran and Hayden, {Stephen M.} and Daniel Ratner and Chunjing Jia and Youssef Nashed and Turner, {Joshua J.}",

note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

month = dec,

doi = "10.1038/s41467-023-41378-4",

language = "English",

volume = "14",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Research",

number = "1",

}

Chitturi, SR, Ji, Z, Petsch, AN, Peng, C, Chen, Z, Plumley, R, Dunne, M, Mardanya, S, Chowdhury, S, Chen, H, Bansil, A, Feiguin, A, Kolesnikov, AI, Prabhakaran, D, Hayden, SM, Ratner, D, Jia, C, Nashed, Y & Turner, JJ 2023, 'Capturing dynamical correlations using implicit neural representations', Nature Communications, vol. 14, no. 1, 5852. https://doi.org/10.1038/s41467-023-41378-4

TY - JOUR

T1 - Capturing dynamical correlations using implicit neural representations

AU - Chitturi, Sathya R.

AU - Ji, Zhurun

AU - Petsch, Alexander N.

AU - Peng, Cheng

AU - Chen, Zhantao

AU - Plumley, Rajan

AU - Dunne, Mike

AU - Mardanya, Sougata

AU - Chowdhury, Sugata

AU - Chen, Hongwei

AU - Bansil, Arun

AU - Feiguin, Adrian

AU - Kolesnikov, Alexander I.

AU - Prabhakaran, Dharmalingam

AU - Hayden, Stephen M.

AU - Ratner, Daniel

AU - Jia, Chunjing

AU - Nashed, Youssef

AU - Turner, Joshua J.

PY - 2023/12

Y1 - 2023/12

N2 - 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 La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

AB - 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 La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

UR - http://www.scopus.com/inward/record.url?scp=85171811031&partnerID=8YFLogxK

U2 - 10.1038/s41467-023-41378-4

DO - 10.1038/s41467-023-41378-4

M3 - Article

C2 - 37730824

AN - SCOPUS:85171811031

SN - 2041-1723

VL - 14

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 5852

ER -

Capturing dynamical correlations using implicit neural representations

Abstract

Funding

Access to Document

Other files and links

Fingerprint

Cite this