High-throughput search for magnetic and topological order in transition metal oxides

Abstract

The discovery of intrinsic magnetic topological order in MnBi2Te4 has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute more than 27,000 unique magnetic orderings for more than 3000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.

Investigating the An+1BnX3n+1 Homologous Series: A New Platform for Studying Magnetic Praseodymium Based Intermetallics

“…Two of the five magnetic transitions of the n = 6 member overlap with two of the transitions in the n = 4 member, with the remaining three transitions overlapping with the transitions found in the n = 5 member, most likely due to their related local environments. − The n = ∞ member, CeCoGe 3 , is a Kondo antiferromagnet ( T K ∼ 100 K) with three magnetic transitions at T = 21 K, T = 12 K, and T = 8 K that resemble the anisotropy of the other n members. The presence of CeCoGe 3 in this series is particularly exciting, as the noncentrosymmetric superconductor has recently been theoretically predicted to be topologically nontrivial. − …”

Big data and machine learning for materials science

“…Of course, this comes at a cost; these complex spaces must be mathematically modelled, and a significant number of the representative patterns must be available as examples for the ML system to learn from. ML classifiers can then be trained to predict material properties, as demonstrated for magnetic materials without requiring first-principles calculations [ 32 ].…”

Artificial intelligence for search and discovery of quantum materials

“…To address this issue, several groups have developed algorithms such as symmetry indicators and spin-orbit spillage that use materials chemistry and symmetry in combination with electronic structures to calculate its topological properties, opening the door to automated screening for new topological insulators and semimetals [66][67][68] . Based on these ideas, several searches through ICSD generated extensive lists of candidates corresponding to various types of topological materials [69][70][71][72][73] . Such developments make the field a fertile ground for the application of ML methods (for an example see Fig.…”