Complex-oxide materials exhibit physical properties that involve the interplay of charge and spin degrees of freedom. However, an ambipolar oxide that is able to exhibit both electron-doped and hole-doped ferromagnetism in the same material has proved elusive. Here we report ambipolar ferromagnetism in LaMnO3, with electron–hole asymmetry of the ferromagnetic order. Starting from an undoped atomically thin LaMnO3 film, we electrostatically dope the material with electrons or holes according to the polarity of a voltage applied across an ionic liquid gate. Magnetotransport characterization reveals that an increase of either electron-doping or hole-doping induced ferromagnetic order in this antiferromagnetic compound, and leads to an insulator-to-metal transition with colossal magnetoresistance showing electron–hole asymmetry. These findings are supported by density functional theory calculations, showing that strengthening of the inter-plane ferromagnetic exchange interaction is the origin of the ambipolar ferromagnetism. The result raises the prospect of exploiting ambipolar magnetic functionality in strongly correlated electron systems.

We report the superconductivity evolution of one unit cell (1-UC) and 2-UC FeSe films on SrTiO 3 (001) substrates with potassium (K) adsorption. By in situ scanning tunneling spectroscopy measurement, we find that the superconductivity in 1-UC FeSe films is continuously suppressed with increasing K coverage, whereas non-superconducting 2-UC FeSe films become superconducting with a gap of ~17 meV or ~11 meV depending on whether the underlying 1-UC films are superconducting or not. This work explicitly reveals that the interface electron-phonon coupling is strongly related to the charge transfer at FeSe/STO interface and plays vital role in enhancing Cooper pairing in both 1-UC and 2-UC FeSe films.

PurposeThe purpose of this paper is to introduce some basic knowledge of statistical learning theory (SLT) based on random set samples in set‐valued probability space for the first time and generalize the key theorem and bounds on the rate of uniform convergence of learning theory in Vapnik, to the key theorem and bounds on the rate of uniform convergence for random sets in set‐valued probability space. SLT based on random samples formed in probability space is considered, at present, as one of the fundamental theories about small samples statistical learning. It has become a novel and important field of machine learning, along with other concepts and architectures such as neural networks. However, the theory hardly handles statistical learning problems for samples that involve random set samples.Design/methodology/approachBeing motivated by some applications, in this paper a SLT is developed based on random set samples. First, a certain law of large numbers for random sets is proved. Second, the definitions of the distribution function and the expectation of random sets are introduced, and the concepts of the expected risk functional and the empirical risk functional are discussed. A notion of the strict consistency of the principle of empirical risk minimization is presented.FindingsThe paper formulates and proves the key theorem and presents the bounds on the rate of uniform convergence of learning theory based on random sets in set‐valued probability space, which become cornerstones of the theoretical fundamentals of the SLT for random set samples.Originality/valueThe paper provides a studied analysis of some theoretical results of learning theory.

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