| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Discovery and Design of Materials
|
| Presentation Title |
Machine-learning-boosted Searching and Optimization of Emergent Quantum Materials |
| Author(s) |
Mingda Li |
| On-Site Speaker (Planned) |
Mingda Li |
| Abstract Scope |
Machine learning (ML) has demonstrated great power in materials science. However, the complex interplay between the charge, spin, orbital and lattice degrees of freedom in quantum materials poses challenge in implementing ML. Here I introduce how ML can accelerate quantum materials research combining measurement techniques. First we will introduce a ML-based topology predictor, where we show that the band topology is indeed encoded in a simple spectral signal with 90%+ accuracy. Hundreds of new topological materials are found accordingly [1]. Second, we use ML to better resolve a fine magnetic effect, which cuts the experimental resolution by a two [2]. Finally, we show how ML can assist in ultrafast diffraction analysis that leads to a panoramic mapping of phonon thermal transport [3]. We conclude by envisioning a variety of quantum-materials-related problems which ML play a key role [4].
[1] arXiv:2003.00994 (Adv. Mater. 2022)
[2] https://aip.scitation.org/doi/10.1063/5.0078814
[3] arXiv:2202.06199
[4] https://aip.scitation.org/doi/10.1063/5.0049111 |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Magnetic Materials |