David Portehault | Searcharxiv

arXiv2026

Synthesis of Ti2B2Clx MBenes in molten salts from theoretical and experimental perspectives

The unique properties and application possibilities of two-dimensional (2D) materials motivates the exploration of different nanolaminated compounds. Here, by using a molten salt approach, we selectively etch Ti2InB2 with ZnCl2 to produce a multilayer (ml) Ti2B2Clx MBene. Scanning transmission electron microscopy, in combination with energy dispersive X-ray, and electron energy loss spectroscopies show that In atoms are completely removed from the precursor upon etching, being replaced by chlorine surface terminations with a coverage 1.1 < x < 1.4. Further, in situ X-ray diffraction indicates a direct biphasic transformation from Ti2InB2 to ml-MBene, with no signs of intermediate phase formation. A computational framework based on density functional theory further corroborates these experimental observations by showing a negative reaction free energy for the formation of ml-MBene, favourable over all competing processes. In addition, A-element substitution into to the 3D Ti2ZnB2 phase is predicted to be endergonic, consistent with the absence of experimental evidence for its formation. Initial Li-ion battery performance evaluation showed a stable discharge capacity similar or better than MAX phases and other borides. Altogether, the theoretical framework combined with materials synthesis and characterization provides a general approach for 2D materials development, for further expansion of the family of 2D materials.

cond-mat.mtrl-sci↗

arXiv2026

Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

Generative diffusion models have emerged as powerful tools for the discovery of inorganic crystal structures, yet steering their sampling process toward user-defined physical and chemical objectives remains challenging. We present a computational framework that integrates adaptive constraint guidance into a pre-trained crystal diffusion model, enabling the generation of candidate structures that satisfy targeted structural and chemical requirements without model retraining. The approach incorporates differentiable constraint functions directly during sampling, providing an interpretable mechanism for expert-driven exploration of the crystal structure space. To assess the reliability of generated candidates, we introduce a multi-stage validation workflow combining descriptor-based analysis, duplicate removal, comparison with reference crystal databases, graph neural network energy prediction, and thermodynamic stability evaluation through convex-hull analysis. The framework is applied to several classes of inorganic compounds and to constraints involving atomic volume, local coordination environments, and near-neighbor structural motifs. Results demonstrate that adaptive guidance effectively redirects the sampling distribution toward structures exhibiting the desired characteristics while preserving chemical plausibility. Subsequent validation reveals which generated candidates remain viable after energetic and thermodynamic screening. The proposed methodology provides a practical and transparent strategy for incorporating expert knowledge into crystal generative models and establishes a general computational framework for constrained materials discovery.

cond-mat.mtrl-sci↗