# Artificial intelligence for search and discovery of quantum materials

@article{Stanev2021ArtificialIF, title={Artificial intelligence for search and discovery of quantum materials}, author={Valentin G. Stanev and Kamal Choudhary and Aaron Gilad Kusne and Johnpierre Paglione and Ichiro Takeuchi}, journal={Communications Materials}, year={2021}, volume={2} }

Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum materials, the rise of new experimental and computational techniques has increased the volume and the speed with which data are collected, and artificial intelligence is poised to impact the exploration of new materials such as superconductors, spin liquids, and topological insulators. This review outlines…

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## References

SHOWING 1-10 OF 174 REFERENCES

### Machine learning in electronic-quantum-matter imaging experiments

- PhysicsNature
- 2019

These ANNs are used to analyse an archive of experimentally derived EQM image arrays from carrier-doped copper oxide Mott insulators, discovering the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state and determining that this state is unidirectional.

### Machine learning spectral indicators of topology

- Physics
- 2020

It is shown that XAS can potentially uncover materials' topology when augmented by machine learning, and the proposed machine learning-empowered XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials.

### Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning

- Materials Sciencenpj Computational Materials
- 2020

Predicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials exhibit a number of unique properties that make…

### High-throughput search for magnetic topological materials using spin-orbit spillage, machine learning, and experiments

- Physics
- 2021

Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation, but very few high-quality…

### Material informatics for layered high-TC superconductors

- PhysicsAPL Materials
- 2020

Superconductors were typically discovered by trial-and-error aided by the knowledge and intuition of individual researchers. In this work, using materials informatics aided by machine learning (ML),…

### Topological quantum chemistry

- PhysicsNature
- 2017

A complete electronic band theory is proposed, which builds on the conventional band theory of electrons, highlighting the link between the topology and local chemical bonding and can be used to predict many more topological insulators.

### Recent advances in high-throughput superconductivity research

- PhysicsSuperconductor Science and Technology
- 2019

The history of high-throughput research paradigm is briefly reviewed, some recent applications of this paradigm in superconductivity research are focused on, and the role these methods can play in all stages of materials development, including high- throughput computation, synthesis, characterization and the emerging field of machine learning for materials is considered.

### Non-Abelian Anyons and Topological Quantum Computation

- Physics
- 2008

Topological quantum computation has emerged as one of the most exciting approaches to constructing a fault-tolerant quantum computer. The proposal relies on the existence of topological states of…

### On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

- PhysicsScientific reports
- 2014

It is shown that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost.

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

- Materials Science, PhysicsScience Advances
- 2020

This work performs a high-throughput band topology analysis of centrosymmetric magnetic materials, calculates topological invariants, and identifies 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagneticTopological insulators.