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

Journal Article · 01 February 2023 · Machine Learning: Science and Technology

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

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

Abstract 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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: Compute and Data Environment for Science (CADES); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division; USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS)
• Grant/Contract Number: AC05-00OR22725; SC0021940
• OSTI ID: 1922965
• Journal Information: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 1 Vol. 4; ISSN 2632-2153
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United Kingdom
• Language: English