A New Magnetic Topological Quantum Material Candidate by Design
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Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces “perovskene,” a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications.
Here, we review recent progress in the study of photogalvanic effects and optical second-harmonic generation in topological and noncentrosymmetric metals.
The transport and optical properties of chiral materials are studied using hydrodynamic and field theory methods combining tools of condensed matter and nuclear physics.
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The properties of naturally occurring materials are not necessarily commensurate with those needed for next-generation devices. Counter to conventional thought, we find magnetic proximity effects can be asymmetric. In fact, our experimental data and DFT calculations indicate this effect is universal, with material and heterostructure specificity being vital for predictive modeling. This work suggests routes towards rational design of device component materials via selective control over spin valley states.