Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Cheng, Yongqiang; Wu, Geoffrey; Pajerowski, Daniel M.; Stone, Matthew B.; Savici, Andrei T.; Li, Mingda; Ramirez-Cuesta, Anibal J.

doi:10.1088/2632-2153/acb315

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Journal Article · Wed Feb 01 00:00:00 EST 2023 · Machine Learning: Science and Technology

DOI:https://doi.org/10.1088/2632-2153/acb315· OSTI ID:1922965

^[1]; Wu, Geoffrey ^[2]; ^[1]; ^[1]; ^[1]; ^[3]; ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Columbia Univ., New York, NY (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.

View Journal Article

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division; USDOE Laboratory Directed Research and Development (LDRD) Program; Compute and Data Environment for Science (CADES); USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS)

Grant/Contract Number:: AC05-00OR22725; SC0021940

OSTI ID:: 1922965

Alternate ID(s):: OSTI ID: 1909261; OSTI ID: 1968697

Journal Information:: Machine Learning: Science and Technology, Vol. 4, Issue 1; ISSN 2632-2153

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

References (19)

Machine learning for neutron scattering at ORNL ^* Doucet, Mathieu; Samarakoon, Anjana M.; Do, Changwoo Machine Learning: Science and Technology, Vol. 2, Issue 2 https://doi.org/10.1088/2632-2153/abcf88	journal	January 2021
Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
Low-temperature vibrational dynamics of fused silica and binary silicate glasses Cai, Ling; Shi, Ying; Hrdina, Ken Physical Review B, Vol. 97, Issue 5 https://doi.org/10.1103/PhysRevB.97.054311	journal	February 2018
Projector augmented-wave method Blöchl, P. E. Physical Review B, Vol. 50, Issue 24, p. 17953-17979 https://doi.org/10.1103/PhysRevB.50.17953	journal	December 1994
A machine learning inversion scheme for determining interaction from scattering Chang, Ming-Ching; Tung, Chi-Huan; Chang, Shou-Yi Communications Physics, Vol. 5, Issue 1 https://doi.org/10.1038/s42005-021-00778-y	journal	February 2022
A comparison of four direct geometry time-of-flight spectrometers at the Spallation Neutron Source Stone, M. B.; Niedziela, J. L.; Abernathy, D. L. Review of Scientific Instruments, Vol. 85, Issue 4 https://doi.org/10.1063/1.4870050	journal	April 2014
Direct Prediction of Phonon Density of States With Euclidean Neural Networks Chen, Zhantao; Andrejevic, Nina; Smidt, Tess Advanced Science, Vol. 8, Issue 12 https://doi.org/10.1002/advs.202004214	journal	March 2021
A database of synthetic inelastic neutron scattering spectra from molecules and crystals Cheng, Yongqiang; Stone, Matthew B.; Ramirez-Cuesta, Anibal J. Scientific Data, Vol. 10, Issue 1 https://doi.org/10.1038/s41597-022-01926-x	journal	January 2023
First principles phonon calculations in materials science Togo, Atsushi; Tanaka, Isao Scripta Materialia, Vol. 108 https://doi.org/10.1016/j.scriptamat.2015.07.021	journal	November 2015
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set Kresse, G.; Furthmüller, J. Physical Review B, Vol. 54, Issue 16, p. 11169-11186 https://doi.org/10.1103/PhysRevB.54.11169	journal	October 1996
From ultrasoft pseudopotentials to the projector augmented-wave method Kresse, G.; Joubert, D. Physical Review B, Vol. 59, Issue 3, p. 1758-1775 https://doi.org/10.1103/PhysRevB.59.1758	journal	January 1999
Machine-learning-assisted insight into spin ice Dy2Ti2O7 Samarakoon, Anjana M.; Barros, Kipton; Li, Ying Wai Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-14660-y	journal	February 2020
Neutron powder diffraction study of Ba3ZnRu2-xIrxO9 (x = 0, 1, 2) with 6H-type perovskite structure Beran, P.; Ivanov, S. A.; Nordblad, P. Solid State Sciences, Vol. 50 https://doi.org/10.1016/j.solidstatesciences.2015.10.011	journal	December 2015
Extraction of interaction parameters for α−RuCl3 from neutron data using machine learning Samarakoon, Anjana M.; Laurell, Pontus; Balz, Christian Physical Review Research, Vol. 4, Issue 2 https://doi.org/10.1103/PhysRevResearch.4.L022061	journal	June 2022
Resolution of VISION, a crystal-analyzer spectrometer Seeger, Philip A.; Daemen, Luke L.; Larese, John Z. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 604, Issue 3 https://doi.org/10.1016/j.nima.2009.03.204	journal	June 2009
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Simulation of Inelastic Neutron Scattering Spectra Using OCLIMAX Cheng, Y. Q.; Daemen, L. L.; Kolesnikov, A. I. Journal of Chemical Theory and Computation, Vol. 15, Issue 3 https://doi.org/10.1021/acs.jctc.8b01250	journal	January 2019
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav Computational Materials Science, Vol. 68 https://doi.org/10.1016/j.commatsci.2012.10.028	journal	February 2013
Machine learning on neutron and x-ray scattering and spectroscopies Chen, Zhantao; Andrejevic, Nina; Drucker, Nathan C. Chemical Physics Reviews, Vol. 2, Issue 3 https://doi.org/10.1063/5.0049111	journal	September 2021

Similar Records

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Journal Article · Tue Jun 25 00:00:00 EDT 2024 · Computer Physics Communications · OSTI ID:2438918

Han, Bowen; Savici, Andrei T.; Li, Mingda; +1 more

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
inelastic neutron scattering
autoencoder
symmetry-aware neural network
structure-property relationship

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Citation Formats

References (19)

Similar Records

Related Subjects