Machine learning force-field models for metallic spin glass
- URL: http://arxiv.org/abs/2311.16964v1
- Date: Tue, 28 Nov 2023 17:12:03 GMT
- Title: Machine learning force-field models for metallic spin glass
- Authors: Menglin Shi, Sheng Zhang, Gia-Wei Chern
- Abstract summary: We present a scalable machine learning framework for dynamical simulations of metallic spin glasses.
A Behler-Parrinello type neural-network model is developed to accurately and efficiently predict electron-induced local magnetic fields.
- Score: 4.090038845129619
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Metallic spin glass systems, such as dilute magnetic alloys, are
characterized by randomly distributed local moments coupled to each other
through a long-range electron-mediated effective interaction. We present a
scalable machine learning (ML) framework for dynamical simulations of metallic
spin glasses. A Behler-Parrinello type neural-network model, based on the
principle of locality, is developed to accurately and efficiently predict
electron-induced local magnetic fields that drive the spin dynamics. A crucial
component of the ML model is a proper symmetry-invariant representation of
local magnetic environment which is direct input to the neural net. We develop
such a magnetic descriptor by incorporating the spin degrees of freedom into
the atom-centered symmetry function methods which are widely used in ML
force-field models for quantum molecular dynamics. We apply our approach to
study the relaxation dynamics of an amorphous generalization of the s-d model.
Our work highlights the promising potential of ML models for large-scale
dynamical modeling of itinerant magnets with quenched disorder.
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