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Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement
Brennon L Shanks1, Jeffrey J Potoff2, Michael P Hoepfner1
1Department of Chemical Engineering, University of Utah, Salt Lake City, UT84112-9202, United States.
The Journal of Physical Chemistry Letters
|December 5, 2022
Summary
A new machine learning method uses neutron scattering to derive accurate atomic potentials for noble gases. This allows precise prediction of material properties and forces from a single measurement.
Area of Science:
- Condensed matter physics
- Materials science
- Computational physics
Background:
- Deriving transferable pair potentials from scattering data is challenging.
- Current methods yield inaccurate thermodynamic predictions for fluids.
- Accurate potentials are crucial for understanding material behavior.
Purpose of the Study:
- To develop a machine learning assisted structure-inversion method for deriving accurate pair potentials.
- To validate the method using neutron scattering data from noble gases (Ne, Ar, Kr, Xe).
- To assess the transferability and predictive power of the derived potentials.
Main Methods:
- Applied a machine learning assisted structure-inversion technique to neutron scattering data.
- Refined pair potentials to match experimental scattering patterns.
- Validated potentials by simulating microstructure and vapor-liquid equilibria.
Main Results:
- Recovered transferable pair potentials for noble gases.
- Accurately reproduced microstructure and vapor-liquid equilibria from triple to critical point.
- Demonstrated that a single neutron scattering measurement is sufficient for prediction.
Conclusions:
- The machine learning method successfully derives accurate and transferable pair potentials.
- These potentials enable precise prediction of macroscopic thermodynamic properties.
- Provides novel insights into local atomic forces in dense monatomic systems.
