arXiv subjects

Arianna Damiani

Publications and source records attributed to Arianna Damiani.

2 recordsLinked to original sources

arXiv2026

Non-Thermal Aging of Supercooled Liquids in Optical Cavities

Aging is a hallmark of disordered materials such as glasses, plastics, and pharmaceuticals, where it often limits long-term stability and performance. In practice, aging is controlled through global parameters like temperature or pressure, which act uniformly on the entire system. Here we introduce a fundamentally different approach, using light confined in optical cavities as a precise and selective tool to guide aging dynamics. We show that a supercooled liquid coupled to an optical cavity undergoes non-thermal aging, where aging is induced by light without a thermal quench. Light selectively pumps fast vibrational modes while the bath temperature remains unchanged, reshaping the slow structural dynamics of the liquid. The cavity-coupled liquid thereby behaves as if it were structurally colder than its surroundings. Exploiting this effective structural cooling together with the timescale separation, we introduce cavity configurational feedback ($\mathrm{C^2F}$) cooling, which uses cavity coupling to reach progressively lower structural temperatures. Our results establish a connection between glass physics and strong light-matter interactions and open a new route toward optical control of aging, glass formation, and nonequilibrium materials dynamics.

cond-mat.stat-mech↗

arXiv2025

Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations

Two-dimensional electronic spectroscopy (2DES) provides rich information about how the electronic states of molecules, proteins, and solid-state materials interact with each other and their surrounding environment. Atomistic molecular dynamics simulations offer an appealing route to uncover how nuclear motions mediate electronic energy relaxation and their manifestation in electronic spectroscopies, but are computationally expensive. Here we show that, by using an equivariant transformer-based machine learning architecture trained with only ~2500 ground state and ~100 excited state electronic structure calculations, one can construct accurate machine-learned potential energy surfaces for both the ground-state electronic surface and excited-state energy gap. We demonstrate the utility of this approach for simulating the dynamics of Nile blue in ethanol, where we experimentally validate and decompose the simulated 2DES to establish the nuclear motions of the chromophore and the solvent that couple to the excited state, connecting the spectroscopic signals to their molecular origin.

physics.chem-ph↗