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Luka Benić

Publications and source records attributed to Luka Benić.

2 recordsLinked to original sources

Fluctuation-driven multi-step charge density wave transition in monolayer TiSe$_2$

The exact microscopic origin, symmetry, and thermal melting mechanism of the charge density wave (CDW) phase in TiSe$_{2}$ remain a subject of intense debate, particularly regarding the presence of chiral structural order and a multi-step phase transition. Here, we resolve the finite-temperature structural dynamics of the monolayer TiSe$_{2}$ using large-scale molecular dynamics simulations driven by an accurate, first-principles-trained machine-learning interatomic potential. We demonstrate that the CDW melting deviates from a conventional second-order phase transition, while it undergoes a two-step melting process characterised by an extended fluctuation regime between $T^{\ast}\approx200$ K and $T_{\mathrm{CDW}}\approx250$ K, with proliferation of topological defects and domain walls, and accompanied by a completely overdamped soft optical phonon. Furthermore, we reveal that anisotropic long-wavelength thermal fluctuations spontaneously stabilise an asymmetric $3Q$ chiral CDW order with $C2$ symmetry. These findings provide a unified microscopic framework for understanding complex fluctuation-driven phase transitions in 2D quantum materials, demonstrating that the intricate CDW physics of TiSe$_{2}$ can be largely captured without invoking excitonic correlations.

cond-mat.mtrl-sci↗

Machine learning model for efficient nonthermal tuning of the charge density wave in monolayer NbSe$_2$

Understanding and controlling the charge density wave (CDW) phase diagram of transition metal dichalcogenides is a long-studied problem in condensed matter physics. However, due to complex involvement of electron and lattice degrees of freedom and pronounced anharmonicity, theoretical simulations of the CDW phase diagram at the density-functional-theory level are often numerically demanding. To reduce the computational cost of first principles modelling by orders of magnitude, we have developed an electronic free energy machine learning model for monolayer NbSe$_2$ that allows changing both electronic and ionic temperatures independently. Our approach relies on a machine learning model of the electronic density of states and zero-temperature interatomic potential. This allows us to explore the CDW phase diagram of monolayer NbSe$_2$ both under thermal and laser-induced nonthermal conditions. Our study provides an accurate estimate of the CDW transition temperature at low cost and can disentangle the role of hot electrons and phonons in nonthermal ultrafast melting process of the CDW phase in NbSe$_2$.

cond-mat.mtrl-sci↗