Universal machine learning framework for defect predictions in zinc blende semiconductors.

We develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in groups IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of approximately 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed neutral state formation energies and charge transition levels of impurities.

Kinetics of Isovalent (Cd) and Aliovalent (In) Cation Exchange in CdMnSe Nanocrystals.

Ion exchange, in which an in-diffusing ion replaces a lattice ion, has been widely exploited as a synthetic tool for semiconductor doping and solid-to-solid chemical transformations, both in bulk and at the nanoscale. Here, we present a systematic investigation of cation-exchange reactions that involve the displacement of Mn from CdSe nanocrystals by Cd or In. For both incoming cations, Mn displacement is spontaneous but thermally activated, following Arrhenius behavior over a broad experimental temperature range.

Insights into scanning probe high-field chemistry of diphenylgermane.

Experiments and simulations are used to elucidate a new class of chemical reactions occurring near the tip-sample interface during high field chemistry of diphenylgermane. Current data during writing and bias dependent growth rate are analyzed, supplemented with data from ionization mass spectrometry, and compared with the simulation results.

Ab initio calculations to model anomalous fluorine behavior.

Implanted fluorine is observed to behave unusually in silicon, manifesting apparent uphill diffusion and reducing diffusion and enhancing activation of boron. In order to investigate fluorine behavior, we calculate the energy of fluorine defect structures in the framework of density functional theory. In addition to identifying the ground-state configuration and diffusion migration barrier of a single fluorine atom in silicon, a set of energetically favorable fluorine defect structures were found (F(n)V(m)).