APS March Meeting Nathan Frey 2020

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The document discusses using high-throughput computational methods and machine learning to discover materials with coexisting magnetic and topological orders. It presents a workflow that couples magnetic and topological property prediction with the existing Materials Project infrastructure. This workflow involves calculating magnetic orderings for over 3,000 transition metal oxides, predicting critical temperatures, and classifying materials using machine learning before determining topological band properties. The results have uncovered 27 ferromagnetic semimetal and 7 antiferromagnetic topological insulator candidates.

ncfrey@lbl.gov
High-throughput Search for
Magnetic and Topological Order in
Transition Metal Oxides
Nathan C. Frey
University of Pennsylvania, Lawrence Berkeley National Laboratory
APS March Meeting 2020
ncfrey@lbl.gov 1

ncfrey@lbl.gov
Can we leverage high-throughput
computational methods and
informatics to discover materials
with coexisting quantum orders?
2

Aims
3
1
Couple magnetic and topological property prediction with existing Materials
Project infrastructure.
2 Discover stable magnetic topological materials.
ncfrey@lbl.gov

The Materials Project
4
Code
Data
Users
Design

Transition Metal Oxide Database
6
Over 3,000 magnetic TMOs
27,000+ individual computed magnetic orderings

Magnetic Ordering Calculation
8
https://www.nature.com/articles/s41524-019-0199-7

Energy Spectrum
9
𝐽𝑒𝑓𝑓~
Δ𝐸
𝑁𝑚2
𝑇 𝑀𝐹𝑇 =
2𝐽𝑒𝑓𝑓
3𝑘 𝐵

Critical Temperature
10
𝐻 = −
1
2
𝑖≠𝑗
𝐽𝑖,𝑗 𝑺𝑖 ⋅ 𝑺𝑗
Magnetic
ion
Exchange
Interaction

Machine Learning Classification
12
ML models trained only on compositional and
structural data, requiring no DFT calculations,
achieve ~80% F1 score on predicting magnetic
ground states.
Still many magnetic materials to evaluate.

High-throughput Band Topology
14
Roger S.K. Mong et al., “Antiferromagnetic topological insulators” [PhysRevB.81.245209]
Dominik Gresch et al., “Z2Pack: Numerical Implementation of Hybrid Wannier Centers for Identifying Topological Materials” [PhysRevB.95.075146]
Alexey A. Soluyanov and David Vanderbilt “Computing topological invariants without inversion symmetry” [PhysRevB.83.235401]
M.G. Vergniory et al., Nature(2019) 566, 480-485. doi:10.1038/s41586-019-0954-4
2 3
J. Munro
1
𝑆 = Θ𝑇1 2

Composite Quantum Materials
16
1. Magnetic topological materials: 27 FM semimetal candidates, 7 AFM TI candidates.
2. Layered magnetic materials: 77 candidates.
3. Next up: Multiferroics? Amorphous materials?

Takeaways
17
1 Databases like Materials Project give us extensive structure-property data
and a broad userbase.
2
New workflows enable magnetic and topological property calculation to
discover stable composite quantum materials.
ncfrey@lbl.gov

Thank you!
18
nc_frey
ncfrey
ncfrey
ncfrey@lbl.gov*
Contact:
Collaborators:
Matthew Horton
Jason Munro
Kristin Persson

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