A physics-based simulator to facilitate reinforcement learning in the RHIC accelerator complex
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43 PARTICLE ACCELERATORS↗
Engineering topics
Nguyen, Linh
Publications and source records attributed to Nguyen, Linh.
The pointing stabilization algorithm for the coherent electron cooling laser transport at RHIC
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43 PARTICLE ACCELERATORS↗
Machine learning magnetism classifiers from atomic coordinates
The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and nonmagnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8% and 73.6%. In particular, a 91% accuracy is reached when predicting no magnetic ordering even if the structure contains magneticelement(s). Ourworkrepresents onestepforwardtosolvingthegrand challenge of full magnetic structure determination.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗