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Machine learning modeling of charge-density-wave recovery after laser melting

Sankha Subhra Bakshi, Yunhao Fan, and Gia-Wei Chern

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Phys. Rev. B 114, 165110 – Published 8 September, 2026

DOI: https://doi.org/10.1103/klpt-f58d

Abstract

We investigate the nonequilibrium dynamics of a laser-pumped two-dimensional spinless Holstein model within a semiclassical framework, focusing on the melting and recovery of long-range charge-density-wave order. Accurately describing this process requires fully nonadiabatic electron–lattice dynamics, which is computationally demanding due to the need to resolve fast electronic motion over long timescales. By analyzing the structure of the lattice force during nonequilibrium evolution, we show that the force naturally separates into a smooth quasiadiabatic component and a residual bathlike contribution associated with fast electronic fluctuations. The quasiadiabatic component depends only on the instantaneous local lattice configuration, with its explicit time dependence encoding the evolving carrier population, and can be efficiently learned using machine-learning techniques, while a minimal Langevin description of the bath term captures the essential features of the recovery dynamics. Combining these elements enables efficient and scalable simulations of long-time nonequilibrium dynamics on large lattices, providing a practical route to access driven correlated systems beyond the reach of direct nonadiabatic approaches.

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Phys. Rev. B 114, 165110– Published 8 September, 2026
  • Received 24 March 2026
  • Accepted 19 August 2026
Article part of CHORUS
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