Phase stability of the nanolaminates V2Ga2C and (Mo1-xVx)2Ga2C from first-principles calculations

Chemical Conversions within the Mo-Ga-C System: Layered Solids with Variable Ga Content

Layered carbides are fascinating compounds due to their enormous structural and chemical diversity, as well as their potential to possess useful and tunable functional properties. Their preparation, however, is challenging and forces synthesis scientists to develop creative and innovative strategies to access high-quality materials. One unique compound among carbides is Mo2Ga2C. Its structure is related to the large and steadily growing family of 211 MAX phases that crystallize in a hexagonal structure (space group P63/mmc) with alternating layers of edge-sharing M6X octahedra and layers of the A-element. Mo2Ga2C also crystallizes in the same space group, with the difference that the A-element layer is occupied by two A-elements, here Ga, that sit right on top of each other (hence named "221" compound). Here, we propose that the Ga content in this compound is variable between 2:2, 2:1, and 2: ≤1 (and 2:0) Mo/Ga ratios. We demonstrate that one Ga layer can be selectively removed from Mo2Ga2C without jeopardizing the hexagonal P63/mmc structure. This is realized by chemical treatment of the 221 phase Mo2Ga2C with a Lewis acid, leading to the "conventional" 211 MAX phase Mo2GaC. Upon further reaction with CuCl2, more Ga is removed and replaced with Cu (instead of fully exfoliating into the Ga-free Mo2CTx MXene), leading to Mo2Ga1-xCuxC still crystallizing with space group P63/mmc, however, with a significantly larger c-lattice parameter. Furthermore, 211 Mo2GaC can be reacted with Ga to recover the initial 221 Mo2Ga2C. All three reaction pathways have not been reported previously and are supported by powder X-ray diffraction (PXRD), electron microscopy, X-ray spectroscopy, and density functional theory (DFT) calculations.

Extending the Chemistry of Layered Solids and Nanosheets: Chemistry and Structure of MAX Phases, MAB Phases and MXenes

MAX phases are layered solids with unique properties combining characteristics of ceramics and metals. MXenes are their two-dimensional siblings that can be synthesized as van der Waals-stacked and multi-/single-layer nanosheets, which possess chemical and physical properties that make them interesting for a plethora of applications. Both families of materials are highly versatile in terms of their chemical composition and theoretical studies suggest that many more members are stable and can be synthesized. This is very intriguing because new combinations of elements, and potentially new structures, can lead to further (tunable) properties. In this review, we focus on the synthesis science (including non-conventional approaches) and structure of members less investigated, namely compounds with more exotic M-, A-, and X-elements, for example nitrides and (carbo)nitrides, and the related family of MAB phases.

Descriptor engineering in machine learning regression of electronic structure properties for 2D materials

We build new material descriptors to predict the band gap and the work function of 2D materials by tree-based machine-learning models. The descriptor's construction is based on vectorizing property matrices and on empirical property function, leading to mixing features that require low-resource computations. Combined with database-based features, the mixing features significantly improve the training and prediction of the models. We find R[Formula: see text] greater than 0.9 and mean absolute errors (MAE) smaller than 0.23 eV both for the training and prediction. The highest R[Formula: see text] of 0.95, 0.98 and the smallest MAE of 0.16 eV and 0.10 eV were obtained by using extreme gradient boosting for the bandgap and work-function predictions, respectively. These metrics were greatly improved as compared to those of database features-based predictions. We also find that the hybrid features slightly reduce the overfitting despite a small scale of the dataset. The relevance of the descriptor-based method was assessed by predicting and comparing the electronic properties of several 2D materials belonging to new classes (oxides, nitrides, carbides) with those of conventional computations. Our work provides a guideline to efficiently engineer descriptors by using vectorized property matrices and hybrid features for predicting 2D materials properties via ensemble models.

Optimizing special quasirandom structure (SQS) models for accurate functional property prediction in disordered 2D alloys

2D materials such as MXenes have garnered attention in a wide field of applications ranging from energy to environment to medical. Properties of 2D materials can be tailored via alloying and in some cases, solid-solutions (disordered alloys) are formed. To predict the disordered alloy properties via first-principles, the model structure needs to imitate the random arrangements of alloyants and yet remains computationally tractable. Using density functional theory and the cluster expansion method, we investigate the accuracy of using of special quasirandom structures (SQSs) for predicting disordered 2D alloy properties, evaluating the effect of SQS supercell size on the prediction quality of formation energies, elastic properties, and structural parameters. We illustrate the findings with 5 different disordered binary [Formula: see text] MXene alloy systems (where M  =  Ti and M'  =  Zr, Hf, V, Nb, or Ta), demonstrating that SQSs around 6-8 times the primitive cell (N  =  6-8) are sufficient to attain convergence in the property predictions versus supercell size. For formation energies, SQSs with N  >  4 are found to reproduce the formation energies of the fully disordered phase within ~2.5 meV. For the simulation of the experimentally-synthesized TiNbCO₂, we find convergence in structural parameters and elastic tensors at N ~ 6. We traced the convergence of the predictions to the convergence in the band structure-related properties via analysis of the electronic densities-of-states and the projected crystal overlap Hamilton population. Our findings suggest that modest sized SQSs would reproduce the properties of disordered MXene alloys. The results should help guide the investigations of structure-property relationships in other disordered 2D materials as well.