Currently, new nanomaterials for high-capacity lithium-ion batteries (LIBs) and sodium- ion batteries (SIBs) are urgently needed. Materials combining porous structure (such as representatives of metal–organic frameworks) and the ability to operate both with lithium and sodium (such as transition-metal dichalcogenides) are of particular interest. Our work reports the computational modelling of a new A’-MoS₂ structure and its application in LIBs and SIBs. The A’-MoS₂ monolayer was dynamically stable and exhibited semiconducting properties with an indirect band gap of 0.74 eV. A large surface area, together with the presence of pores resulted in a high capacity of the A’-MoS₂ equal to ~391 mAg⁻¹ at maximum filling for both Li and Na atoms. High adsorption energies and small values of diffusion barriers indicate that the A’-MoS₂ is promising in the application of anode material in LIBs and SIBs.

The exploration of van der Waals (vdW) materials, renowned for their unique optical properties, is pivotal for advanced photonics. These materials exhibit exceptional optical anisotropy, both in-plane and out-of-plane, making them an ideal platform for novel photonic applications. However, the manual search for vdW materials with giant optical anisotropy is a labor-intensive process unsuitable for the fast screening of materials with unique properties. Here, we leverage geometrical and machine learning (ML) approaches to streamline this search, employing deep learning architectures, including the recently developed Atomistic Line Graph Neural Network. Within the geometrical approach, we clustered vdW materials based on in-plane and out-of-plane birefringence values and correlated optical anisotropy with crystallographic parameters. The more accurate ML model demonstrates high predictive capability, validated through density functional theory and ellipsometry measurements. Experimental verification with 2H-MoTe₂ and CdPS₃ confirms the theoretical predictions, underscoring the potential of ML in discovering and optimizing vdW materials with unprecedented optical performance.

Accurate and efficient prediction of the optical properties of two-dimensional (2D) materials is crucial for photonic applications but remains a challenging task due to discrepancies between theoretical and experimental approaches. Here, we present a physics-guided machine learning (ML) framework for accelerated screening of 2D materials. It combines first-principles density functional theory (DFT) calculations and graph neural network models backed by experimental spectroscopic validation and Cauchy-model integration. Within this framework, we collected a database of more than 1000 monolayers of transition metal dichalcogenides (TMDs) along with their optical properties. We also proposed a universal method for defining a physically meaningful thickness of two-dimensional structures, which enabled a correction of the optical properties obtained from PBE-based density functional theory. Using the collected database, we developed a Cauchy-model-based machine learning model to calculate refractive indices in the near-infrared (near-IR) region (755-1064 nm). The developed approach reflects correlations between the atomic structures of monolayers and their optical properties, which are confirmed by extensive testing against independent 2D materials databases. In this way, our ML-driven strategy offers a powerful tool for the rapid screening of novel monolayer materials with tailored optical functionalities, significantly accelerating the discovery and design of next-generation photonic materials. As an application, we further demonstrate how high-refractive-index candidates such as Bi₂Te₂Se enable enhanced field confinement and long crosstalk lengths in monolayer waveguides, highlighting their promise for integrated photonics.