Instantaneous normal modes in liquids: a heterogeneous-elastic-medium approach
The concept of vibrational density of states in glasses has been mirrored in liquids by the instantaneous-normal-mode spectrum. While in glasses instantaneous configurations correspond to minima of the potential-energy hypersurface and all eigenvalues of the associated Hessian matrix are therefore positive, in liquids this is no longer true, and modes corresponding to both positive and negative eigenvalues exist. The instantaneous-normal-mode spectrum has been numerically investigated in the past, and it has been demonstrated to bring important information on the liquid dynamics. A systematic deeper theoretical understanding is now needed. Heterogeneous-elasticity theory has proven to be successful in explaining many details of the low-frequency excitations in glasses, ranging from the thoroughly studied boson peak, down to the more elusive non-phononic excitations observed in numerical simulations at the lowest frequencies. Here we present an extension of heterogeneous-elasticity theory to the liquid state, and show that the outcome of the theory agrees well to the results of extensive molecular-dynamics simulations of a model liquid at different temperatures. We show that the spectral shape strongly depends on temperature, being symmetric at high temperatures and becoming rather asymmetric at low temperatures, close to the dynamical critical temperature. Most importantly, we demonstrate that the theory naturally reproduces a surprising phenomenon, a zero-energy spectral singularity with a cusp-like character developing in the vibrational spectra upon cooling. This feature, known from a few previous numerical studies, has been generally overlooked in the past due to a misleading representation of the data. We provide a thorough analysis of this issue, based on both very accurate predictions of our theory, and computational studies of model liquid systems with extended size.
- Pub Date:
- February 2023
- Condensed Matter - Disordered Systems and Neural Networks;
- Condensed Matter - Materials Science;
- Condensed Matter - Statistical Mechanics