Machine Learning Model for Efficient Nonthermal Tuning of the Charge Density Wave in Monolayer NbSe₂

Area of Science:

• Condensed matter physics
• Materials science
• Computational physics

Background:

• Understanding charge density wave (CDW) phase diagrams in transition-metal dichalcogenides is crucial but computationally challenging.
• First-principles simulations of CDW phases are often limited by high computational costs due to complex electron-lattice interactions and anharmonicity.

Purpose of the Study:

• To develop a computationally efficient method for simulating the CDW phase diagram of monolayer niobium diselenide (NbSe2).
• To enable exploration of CDW phase behavior under both thermal and nonthermal conditions, including laser-induced effects.

Main Methods:

• Developed an electronic free-energy machine learning model for monolayer NbSe2, incorporating electronic temperature control.
• Modeled ionic temperature using the stochastic self-consistent harmonic approximation.
• Utilized a machine learning model of the electronic density of states and zero-temperature interatomic potential.

Main Results:

• Achieved orders-of-magnitude reduction in computational cost for first-principles modeling of CDW phases.
• Enabled accurate, low-cost estimation of the CDW transition temperature in NbSe2.
• Provided insights into the roles of hot electrons and phonons in the ultrafast melting of the CDW phase.

Conclusions:

• The developed machine learning approach offers a powerful and efficient tool for studying CDW phase diagrams.
• This method facilitates detailed investigation of CDW dynamics under both equilibrium and non-equilibrium conditions.
• The findings contribute to a deeper understanding of electron-phonon coupling and phase transitions in materials like NbSe2.