Oxygen Deficient α-MoO3 with Promoted Adsorption and State-Quenching of H2O for Gas Sensor: A DFT Study

Semiconducting oxides with reducible cations are ideal platforms for various functional applications in nanoelectronics and catalysts. In this work, we explore an ultrathin oxide under the atomic limit with monolayer...

Structure of an ultrathin oxide film solved by machine learning enhanced global optimization

Determination of the atomic structure of solid surfaces is a challenge that has resisted solution despite advancements in experimental methods. Theory-based global optimization has the potential to revolutionize the field by providing reliable structure models as the basis for interpretation of experiments and for prediction of material properties. So far, however, the approach has been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. We demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4 x 4) surface oxide on Pt3Sn(111)--based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.

Unified Model of Shot Noise in the Tunneling Current in Sub-10 nm MOSFETs

A single unified analytical model is presented to predict the shot noise for both the source-to-drain (SD) and the gate tunneling current in sub-10 nm MOSFETs with ultrathin oxide. Based on the Landauer formula, the model is constructed from the sequential tunneling flows associated with number fluctuations. This approach provides the analytical formulation of the shot noise as a function of the applied voltages. The model performs well in predicting the Fano factor for shot noise in the SD and gate tunneling currents.