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

Journal Article · 25 June 2024 · Computer Physics Communications

DOI: https://doi.org/10.1016/j.cpc.2024.109288 · OSTI ID:2438918

Han, Bowen  ^[1]; Savici, Andrei T.  ^[1]; Li, Mingda ^[2]; Cheng, Yongqiang  ^[1]

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1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. Finally, we expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC05-00OR22725; SC0021940; AC02-05CH11231
• OSTI ID: 2438918
• Journal Information: Computer Physics Communications, Journal Name: Computer Physics Communications Vol. 304; ISSN 0010-4655
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English