ISIS Neutron and Muon Source Data Journal

Assessing the quality of machine learned force fields for dynamical properties using quasi-elastic neutron scattering

Abstract: The richness of the insight provided from the combined use of simulation and neutron spectroscopy has been extensively proven in recent times. Molecular Dynamics has the capability of studying the same length and time scales as that of Quasi-elastic Neutron Scattering (QENS), however the most accurate descriptions of intermolecular forces provided by ab initio methods are unfortunately severely limited by current computational power. The recent emergence of Machine Learning Force fields presents the possibility of retaining elements of an ab initio level description, but allowing the simulation of nano-scale dynamics required for the comparison with experimental QENS results. Early results show that these new models are capable of correctly predicting structural properties, however as yet there have not been robust tests on the accuracy of their dynamics. We propose to perform the first tests of these models for their nano-scale dynamics to assess whether these models could represent a paradigm shift in how we analyse QENS data.

Principal Investigator: Dr Andrew McCluskey
Local Contact: Dr Jeff Armstrong
Experimenter: Dr Kit McColl
Experimenter: Mr Harry Richardson

DOI: 10.5286/ISIS.E.RB2420517

ISIS Experiment Number: RB2420517

Publisher: STFC ISIS Neutron and Muon Source

Data format: RAW/Nexus
Select the data format above to find out more about it.

Data Citation

The recommended format for citing this dataset in a research publication is as:
[author], [date], [title], [publisher], [doi]

For Example:
Dr Andrew McCluskey et al; (2025): Assessing the quality of machine learned force fields for dynamical properties using quasi-elastic neutron scattering, STFC ISIS Neutron and Muon Source, https://doi.org/10.5286/ISIS.E.RB2420517

Data is released under the CC-BY-4.0 license.