To speed up the implementation of the two-dimensional materials in the development of potential biomedical applications, the toxicological aspects toward human health need to be addressed. Due to time-consuming and expensive analysis, only part of the continuously expanding family of 2D materials can be tested in vitro. The machine learning methods can be used—by extracting new insights from available biological data sets, and provide further guidance for experimental studies. This study identifies the most relevant highly surface-specific features that might be responsible for cytotoxic behavior of 2D materials, especially MXenes. In particular, two factors, namely, the presence of transition metal oxides and lithium atoms on the surface, are identified as cytotoxicity-generating features. The developed machine learning model succeeds in predicting toxicity for other 2D MXenes, previously not tested in vitro, and hence, is able to complement the existing knowledge coming from in vitro studies. Thus, we claim that it might be one of the solutions for reducing the number of toxicological studies needed, and allows for minimizing failures in future biological applications.

We study the effects of covalent functionalization of single wall carbon nanotubes (CNT) on their elastic properties. We consider simple organic molecules –NH, –NH₂, –CH₂, –CH₃, –OH attached to CNTs' surface at various densities. The studies are based on the first principles calculations in the framework of density functional theory. We have determined the changes in the geometry and the elastic moduli of the functionalized CNTs as a function of the density of adsorbed molecules. It turns out that elastic moduli diminish with increasing concentration of adsorbents, however, the functionalized CNTs remain strong enough to be suitable for reinforcement of composites. The strongest effect is observed for CNTs functionalized with –CH₂ radical, where the Young's modulus of the functionalized system is 30% smaller than in the pristine CNTs.

We present a thorough theoretical study of ordering phenomena in nitride ternary alloys GaxIn1$-${}xN, AlxIn1$-${}xN, and AlxGa1$-${}xN. Using the Monte Carlo approach and energetics based on the Keating model, we analyze the influence of various factors on ordering in bulk crystals and epitaxial layers. We characterize the degree of both short-range order (SRO) and long-range order (LRO) for different compositions, temperatures, and for substrates associated with different epitaxial strain. For the description of the SRO, the Warren-Cowley parameters related to the first four coordination shells are used. The LRO is detected by means of the introduced sim-LRO parameter, based on the Bragg-Williams approach. The description of the observed long-range ordering patterns and conditions for their occurrence follows.

We have performed first-principles local density calculations of several interfaces between GaN and AlN and studied their formation enthalpy and band offsets and the charge pileup at polar interfaces. Monte Carlo studies of the electrical characteristics of submicron HFET structures reveal that the pyro- and piezoelectric moments of the nitrides are a key property for designing and optimizing future devices.