Condensed-Matter Physics & Materials Science Seminar

Machine learning (ML) has demonstrated great power in materials science in the past few years. In this seminar, I will introduce how ML can augment the x-ray, neutron, and electron scattering analysis by revealing the buried information and accelerating materials design. I'll provide three examples in elastic scattering, absorption spectra, and inelastic scattering. In elastic scattering, we introduce how ML can lead to robust structural information extractions in topological insulator heterostructures beyond analytical fitting models; in absorption spectroscopy, we introduce a spectra-based classifier from simple probe for topology [1]; for inelastic scattering, we introduce an efficient predictor of elementary excitations even in alloy space with ab initio accuracy but low computational cost [2]. We further show how machine learning can assist time-resolved diffraction analysis and conclude by showing a variety of problems machine learning may solve in neutron, x-ray and electron spectroscopic researches [3].

[1] https://arxiv.org/abs/2003.00994
[2] https://onlinelibrary.wiley.com/doi/10.1002/advs.202004214
[3] https://arxiv.org/abs/2102.03024