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

Benić, Luka; Grasselli, Federico; Ben Mahmoud, Chiheb; Novko, Dino; Lončarić, Ivor (2025) Machine Learning Model for Efficient Nonthermal Tuning of the Charge Density Wave in Monolayer NbSe2. Journal of Chemical Theory and Computation, 21 (16). pp. 8130-8141. ISSN 1549-9618

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Abstract

Understanding and controlling the charge density wave (CDW) phase diagram of transition-metal dichalcogenides are long-studied problems in condensed matter physics. However, due to the 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 modeling by orders of magnitude, we have developed an electronic free-energy machine learning model for monolayer NbSe2 that allows us to control the electronic temperature as a parameter of the model. The ionic temperature is modeled via the stochastic self-consistent harmonic approximation. 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 NbSe2 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 the nonthermal ultrafast melting process of the CDW phase in NbSe2.

Item Type:	Article
Uncontrolled Keywords:	charge density wave; machine learning interatomic potential
Subjects:	NATURAL SCIENCES > Physics NATURAL SCIENCES > Physics > Condensed Matter Physics
Divisions:	Theoretical Physics Division
Projects:	Project title Project leader Project code Project type Povećanje prostorne i vremenske skale modeliranja materijala iz prvih principa pomoću strojnog učenja-ExtMatModelML Ivor Lončarić UIP-2020-02-5675 HRZZ
Depositing User:	Ivor Lončarić
Date Deposited:	23 Sep 2025 13:06
URI:	http://fulir.irb.hr/id/eprint/10013
DOI:	10.1021/acs.jctc.5c00959

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