Correction to “First-Principles Assessment of CdTe as a Tunnel Barrier at the α-Sn/InSb Interface”
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Engineering topics
Yu, Maituo
Publications and source records attributed to Yu, Maituo.
First-Principles Assessment of CdTe as a Tunnel Barrier at the α-Sn/InSb Interface
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
First principles study of the electronic structure of the Ni 2 MnIn / InAs and Ti 2 MnIn / InSb interfaces
We present a first principles study of the electronic and magnetic properties of epitaxial interfaces between the Heusler compounds, Ti 2 MnIn and Ni 2 MnIn, and the III-V semiconductors, InSb and InAs, respectively. We use density functional theory (DFT) with a machine-learned Hubbard U correction determined by Bayesian optimization. Here, we evaluate these interfaces for prospective applications in Majorana-based quantum computing and spintronics. In both interfaces, states from the Heusler penetrate into the gap of the semiconductor, decaying within a few atomic layers. The magnetic interactions at the interface are weak and local in space and energy. Magnetic moments of less than 0.1 µB are induced in the two atomic layers closest to the interface. The induced spin polarization around the Fermi level of the semiconductor decays within a few atomic layers. The decisive factor for the induced spin polarization around the Fermi level of the semiconductor is the spin polarization around the Fermi level in the Heusler, rather than the overall magnetic moment. As a result, the ferrimagnetic narrow-gap semiconductor Ti 2 MnIn induces a more significant spin polarization in the InSb than the ferromagnetic metal Ni 2 MnIn induces in the InAs. This is explained by the position of the transition metal d states in the Heusler with respect to the Fermi level. Based on our results, these interfaces are unlikely to be useful for Majorana devices but could be of interest for spintronics.
36 MATERIALS SCIENCE↗
Finding predictive models for singlet fission by machine learning
Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.
36 MATERIALS SCIENCE↗
Best practices for first-principles simulations of epitaxial inorganic interfaces
Abstract At an interface between two materials physical properties and functionalities may be achieved, which would not exist in either material alone. Epitaxial inorganic interfaces are at the heart of semiconductor, spintronic, and quantum devices. First principles simulations based on density functional theory (DFT) can help elucidate the electronic and magnetic properties of interfaces and relate them to the structure and composition at the atomistic scale. Furthermore, DFT simulations can predict the structure and properties of candidate interfaces and guide experimental efforts in promising directions. However, DFT simulations of interfaces can be technically elaborate and computationally expensive. To help researchers embarking on such simulations, this review covers best practices for first principles simulations of epitaxial inorganic interfaces, including DFT methods, interface model construction, interface structure prediction, and analysis and visualization tools.
Physics↗