Thanasee Thanasarnsurapong

On-the-fly machine learning force fields (MLFFs), trained using ab initio molecular dynamics, offer a fast and accurate alternative to density functional theory (DFT) for predicting lattice thermal conductivity (κ _L ) in two-dimensional materials. This study focuses on MoS₂ monolayer, where the MLFF demonstrates excellent agreement with DFT, yielding low root-mean-square errors (RMSEs) of 0.303 meV·atom^–1 for energies and 0.013 eV·Å^–1 for atomic forces. Leveraging this accuracy and the computational efficiency of MLFFs, we investigated the effects of biaxial tensile strain on κ _L . Our results show an 80% reduction in κ _L under 16% strain, which is attributed to suppressed phonon group velocities (v _λ), shortened phonon relaxation times (τ_λ), and enhanced anharmonicity. These mechanisms were understood through detailed analysis of strain-induced changes in phonon dispersions, τ_λ, and v _λ. This study on MoS₂ monolayer reveals that MLFFs provide a powerful and efficient framework for calculating κ _L in low-dimensional systems under strain, significantly advancing our understanding of strain-modulated thermal transport.

We propose a novel two-dimensional (2D) tetrahexagonal NiN₂ (tetrahex-NiN₂) monolayer, derived through a full Stone–Wales transformation of penta-NiN₂, which converts an all-pentagonal lattice into a network of four- and six-membered rings. First-principles calculations confirm that this new phase is dynamically and thermally stable. Tetrahex-NiN₂ exhibits pronounced mechanical anisotropy and shortened N–N bonds, indicating significant structural reorganization. Hybrid functional calculations reveal metallic character, with strong hybridization between Ni-d and N-p orbitals near the Fermi level. Furthermore, the material demonstrates broadband and anisotropic optical absorption across the visible spectrum, suggesting promising potential for optoelectronic and photothermal applications. These findings position tetrahex-NiN₂ as a stable and tunable 2D nitride and highlight the effectiveness of topological reconstruction as a strategy for engineering novel 2D quantum phases.

A comprehensive understanding of rare-earth-doped MoS₂ is essential for the rational design of catalysts with increasing active sites to improve hydrogen evolution reaction (HER) performance. In this work, we synthesized Dy-doped MoS₂ (MoS₂/Dy) via a citrate-assisted hydrothermal method with varying Dy loadings. Citrate plays a vital role in stabilizing Dy ions in a solution mixture and preventing their precipitation. The successful formation of 2H-phase MoS₂ and its morphology were confirmed by X-ray diffraction (XRD) and electron microscopy analyses. The Dy-induced alteration in the electronic structure of MoS₂ and the local chemical environment of Dy were also elucidated by X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS). Upon variation of Dy doping, a decrease in the Mo:S ratio was observed with the presence of sulfur vacancies, which further enriches the exposure of the active sites. Among all synthesized catalysts, MoS₂ with the optimum Dy loading (7 wt %) exhibits the superior electrocatalytic performance in 0.5 M H₂SO₄, delivering a low overpotential of 202 mV at a current density of 10 mA cm^–2 and a favorable Tafel slope of 53.9 mV dec^–1. The MoS₂/Dy catalyst also demonstrates excellent durability, maintaining stable HER performance over 36 h without significant degradation. Moreover, density functional theory (DFT) calculations suggest that hydrogen adsorption near the sulfur vacancy and Dy atom provides the optimum value of −0.30 eV for HER. These results highlight the synergistic relation of Dy doping and sulfur vacancies in enhancing the electrocatalytic activity of MoS₂, positioning it as a promising candidate for sustainable hydrogen production in acid media.

Traditionally, lattice thermal conductivity is evaluated using the phonon Boltzmann transport equation (PBTE) in combination with density functional theory (DFT) calculations. However, this approach is computationally intensive. In this study, we predicted lattice thermal conductivity of Ti₂C and Ti₃C₂ MXenes, along with their functionalized variants featuring surface terminations (O, F, OH), by using active DFT-based on-the-fly machine learning force fields (MLFF). The predicted thermal conductivities of Ti₂C and Ti₃C₂ are 73.10 and 101.15 W m^–1 K^–1, respectively, close to previously calculated DFT values. The introduction of surface functional groups significantly reduces the lattice thermal conductivity. Furthermore, the MLFF-based predictions of lattice thermal conductivity are tens to thousands of times faster than conventional DFT calculations, dramatically accelerating the study of thermal transport in MXenes. This efficiency highlights the potential of MLFF as a powerful tool for exploring and optimizing the thermal properties of two-dimensional materials.

We have investigated the elastic properties and lattice thermal conductivity of Nb ₂ CX ₂ MXenes using MLFF. MLFF outperforms DFT in efficiency, revealing how surface functionalization tunes thermal transport for next-gen energy applications.