Elton Ogoshi

TL;DR: In this paper, the authors proposed a causal design principle for spin-orbit-induced spin splitting of energy bands in low-symmetry compounds (the Rashba effect) and identified 34 rationally designed strong Rashba compounds.

TL;DR: In this article , a machine learning-based strategy was proposed to predict and understand magnetic ordering in 2D materials using a random forest and the Shapley additive explanation method with material maps defined by atomic features predicting the magnetic ordering (ferromagnetic or antiferromagnetic).

TL;DR: In this paper , the authors proposed a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization, and applied this process to the C2DB database, identifying and classifying 358 2D materials according to spin splitting type at the valence and/or conduction bands.

TL;DR: In this article, the authors proposed a causal design principle for strong-Rashba properties of topological insulators and showed that the presence of energy band anti-crossing provides a design principle of compounds with large Rashba coefficients, leading to the identification via first-principles calculations of 34 rationally designed strong RPs.

TL;DR: In this paper , the authors proposed a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization, and applied this process to the C2DB database, identifying and classifying 358 2D materials according to spin splitting type at the valence and/or conduction bands.