Machine learning magnetism classifiers from atomic coordinates
Helena A. Merker,
Harry Heiberger,
Linh Nguyen,
Tongtong Liu,
Zhantao Chen,
Nina Andrejevic,
Nathan C. Drucker,
Ryotaro Okabe,
Song Eun Kim,
Yao Wang,
Tess Smidt,
Mingda Li
Affiliations
Helena A. Merker
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Harry Heiberger
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Linh Nguyen
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Tongtong Liu
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Zhantao Chen
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Nina Andrejevic
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Nathan C. Drucker
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Applied Physics, Harvard University, Cambridge, MA 02138, USA
Ryotaro Okabe
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Song Eun Kim
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Yao Wang
Department of Physics, Clemson University, Clemson, SC 29634, USA
Tess Smidt
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Corresponding author
Mingda Li
Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Corresponding author
Summary: 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 non-magnetic) 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 magnetic element(s). Our work represents one step forward to solving the grand challenge of full magnetic structure determination.
