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Published March 18, 2024 | Version 0.1.0
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INSPIRED: Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design

Creators

  • 1. ROR icon Oak Ridge National Laboratory
  • 2. ROR icon Massachusetts Institute of Technology

Description

INSPIRED is a graphic user interface (GUI) that performs rapid prediction and calculation of phonons and inelastic neutron scattering (INS) spectra. It consists of three modules. The "Predictor" module uses a symmetry-aware neural network (coupled with an autoencoder) [1-3] to perform direct prediction of total/partial phonon density of states and powder 1D/2D INS spectra from a given structure. The "DFT database" module uses pre-calculated force constants from density functional theory (DFT) [4] to perform INS simulations for single crystals and powders (for the crystals available in the database). The "MLFF" module uses pre-trained universal force fields [8-12] to perform structural optimization, phonon calculation, and INS simulations for single crystals and powders for any given crystal. The predicted/calculated results are saved in CSV files and can be visualized with the GUI. INSPIRED is developed to be a convenient tool for INS experimental planning, steering, and quick data analysis.

This repository contains three files:

1. A tarball file (dftdb.tar.gz) containing the DFT database (currently with 11979 crystals)

2. A tarball file (model.tar.gz) containing the ML models

3. A VirtualBox appliance file (inspired_vm.ova) to run INSPIRED as a virtual machine.

Instructions on how to use these files, as well as the rest part of the software, can be found on the GitHub page. 

Files

Files (8.0 GB)

Name Size Download all
md5:f8c4fd36938731f819bccf70307b7f07
463.2 MB Download
md5:1a47f2af4387aa22076e2ab24128d085
6.7 GB Download
md5:fe636452058f826ff0d70216ddae481d
888.0 MB Download

Additional details

Related works

Is derived from
Dataset: 10.5281/zenodo.7438040 (DOI)

Funding

United States Department of Energy

Software

Repository URL
https://github.com/cyqjh/inspired
Programming language
Python

References

  • 1. Cheng, Y.; Wu, G.; Pajerowski, D. M.; Stone, M. B.; Savici, A. T.; Li, M.; Ramirez-Cuesta, A. J., Direct prediction of inelastic neutron scattering spectra from the crystal structure. Machine Learning: Science and Technology 2023, 4 (1), 015010.
  • 2. Cheng, Y.; Stone, M. B.; Ramirez-Cuesta, A. J., A database of synthetic inelastic neutron scattering spectra from molecules and crystals. Scientific Data 2023, 10 (1), 54.
  • 3. Chen, Z.; Andrejevic, N.; Smidt, T.; Ding, Z.; Xu, Q.; Chi, Y.-T.; Nguyen, Q. T.; Alatas, A.; Kong, J.; Li, M., Direct Prediction of Phonon Density of States With Euclidean Neural Networks. Advanced Science 2021, 8 (12), 2004214.
  • 4. Togo, A. http://phonondb.mtl.kyoto-u.ac.jp/. (accessed 08/30/2022).
  • 5. Togo, A.; Tanaka, I., First principles phonon calculations in materials science. Scripta Materialia 2015, 108, 1-5.
  • 6. Cheng, Y.; Daemen, L.; Kolesnikov, A.; Ramirez-Cuesta, A., Simulation of inelastic neutron scattering spectra using OCLIMAX. Journal of chemical theory and computation 2019, 15 (3), 1974-1982.
  • 7. Hjorth Larsen, A.; Jørgen Mortensen, J.; Blomqvist, J.; Castelli, I. E.; Christensen, R.; Dułak, M.; Friis, J.; Groves, M. N.; Hammer, B.; Hargus, C.; Hermes, E. D.; Jennings, P. C.; Bjerre Jensen, P.; Kermode, J.; Kitchin, J. R.; Leonhard Kolsbjerg, E.; Kubal, J.; Kaasbjerg, K.; Lysgaard, S.; Bergmann Maronsson, J.; Maxson, T.; Olsen, T.; Pastewka, L.; Peterson, A.; Rostgaard, C.; Schiøtz, J.; Schütt, O.; Strange, M.; Thygesen, K. S.; Vegge, T.; Vilhelmsen, L.; Walter, M.; Zeng, Z.; Jacobsen, K. W., The atomic simulation environment—a Python library for working with atoms. Journal of Physics: Condensed Matter 2017, 29 (27), 273002.
  • 8. Batatia, I.; Kovacs, D. P.; Simm, G.; Ortner, C.; Csányi, G., MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems 2022, 35, 11423-11436.
  • 9. Batatia, I.; Batzner, S.; Kovács, D. P.; Musaelian, A.; Simm, G. N.; Drautz, R.; Ortner, C.; Kozinsky, B.; Csányi, G., The design space of E (3)-equivariant atom-centered interatomic potentials. arXiv preprint arXiv:2205.06643 2022.
  • 10. Batatia, I.; Benner, P.; Chiang, Y.; Elena, A. M.; Kovács, D. P.; Riebesell, J.; Advincula, X. R.; Asta, M.; Baldwin, W. J.; Bernstein, N., A foundation model for atomistic materials chemistry. arXiv preprint arXiv:2401.00096 2023.
  • 11. Deng, B.; Zhong, P.; Jun, K.; Riebesell, J.; Han, K.; Bartel, C. J.; Ceder, G., CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nature Machine Intelligence 2023, 5 (9), 1031-1041.
  • 12. Chen, C.; Ong, S. P., A universal graph deep learning interatomic potential for the periodic table. Nature Computational Science 2022, 2 (11), 718-728.