The electric and non-volatile control of the spin texture in semiconductors would represent a fundamental step towards novel electronic devices combining memory and computing functionalities. Recently, GeTe has been theoretically proposed as the father compound of a new class of materials, namely ferroelectric Rashba semiconductors. They display bulk bands with giant Rashba-like splitting, due to the inversion symmetry breaking arising from the ferroelectric polarization, thus allowing for a ferroelectric control of the spin. Here we provide the experimental demonstration of the correlation between ferroelectricity and spin texture. A surface engineering strategy is used to set two opposite pre-defined uniform ferroelectric polarizations, inward and outward, as monitored by Piezoresponse Force Microscopy. Spin and angular resolved photoemission experiments show that these GeTe(111) surfaces display opposite sense of circulation of spin in bulk Rashba bands. Furthermore, we demonstrate the crafting of non-volatile ferroelectric patterns in GeTe films at the nanoscale, by using the conductive tip of an atomic force microscope. Based on the intimate link between ferroelectric polarization and spin in GeTe, ferroelectric patterning paves the way to the investigation of devices with engineered spin configurations.

The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi‐dimensional parameter space and massive data sets involved is emblematic of complex, resource‐intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data‐driven approach and subset of artificial intelligence, is a potential game‐changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine‐assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Here, we present an integrated ultra-high vacuum apparatus—named MBE-Cluster —dedicated to the growth and in situ structural, spectroscopic, and magnetic characterization of complex materials. Molecular Beam Epitaxy (MBE) growth of metal oxides, e.g., manganites, and deposition of the patterned metallic layers can be fabricated and in situ characterized by reflection high-energy electron diffraction, low-energy electron diffraction, Auger electron spectroscopy, x-ray photoemission spectroscopy, and azimuthal longitudinal magneto-optic Kerr effect. The temperature can be controlled in the range from 5 K to 580 K, with the possibility of application of magnetic fields H up to ±7 kOe and electric fields E for voltages up to ±500 V. The MBE-Cluster operates for in-house research as well as user facility in combination with the APE beamlines at Sincrotrone-Trieste and the high harmonic generator facility for time-resolved spectroscopy.