On the polyamorphism of fullerite-based orientational glasses

A. N. Aleksandrovskii; A. S Bakai; D. Cassidy; A. V. Dolbin; V. B. Esel'son; G. E. Gadd; V. G. Gavrilko; V. G. Manzhelii; S. Moricca; B. Sundqvist

doi:10.1063/1.1925371

arXiv · cond-mat/0407791

On the polyamorphism of fullerite-based orientational glasses

A. N. Aleksandrovskii·A. S Bakai·D. Cassidy·A. V. Dolbin·V. B. Esel'son·G. E. Gadd·V. G. Gavrilko·V. G. Manzhelii·S. Moricca·B. Sundqvist

View original source ↗Read source document ↗Export metadata ↓

Abstract

The dilatometric investigation in the temperature range of 2-28K shows that a first-order polyamorphous transition occurs in the orientational glasses based on C60 doped with H2, D2 and Xe. A polyamorphous transition was also detected in C60 doped with Kr and He. It is observed that the hysteresis of thermal expansion caused by the polyamorphous transition (and, hence, the transition temperature) is essentially dependent on the type of doping gas. Both positive and negative contributions to the thermal expansion were observed in the low temperature phase of the glasses. The relaxation time of the negative contribution occurs to be much longer than that of the positive contribution. The positive contribution is found to be due to phonon and libron modes, whilst the negative contribution is attributed to tunneling states of the C60 molecules. The characteristic time of the phase transformation from the low-T phase to the high-T phase has been found for the C60-H2 system at 12K. A theoretical model is proposed to interpret these observed phenomena. The theoretical model proposed, includes a consideration of the nature of polyamorphism in glasses, as well as the thermodynamics and kinetics of the transition. A model of non-interacting tunneling states is used to explain the negative contribution to the thermal expansion. The experimental data obtained is considered within the framework of the theoretical model. From the theoretical model the order of magnitude of the polyamorphous transition temperature has been estimated. It is found that the late stage of the polyamorphous transformation is described well by the Kolmogorov law with an exponent of n=1. At this stage of the transformation, the two-dimensional phase boundary moves along the normal, and the nucleation is not important.

Explore related subjects

cond-mat.dis-nn cond-mat.mtrl-sci

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

A. N. Aleksandrovskii, A. S Bakai, D. Cassidy, A. V. Dolbin, V. B. Esel'son, G. E. Gadd, V. G. Gavrilko, V. G. Manzhelii, S. Moricca, B. Sundqvist. 2004-09-28. On the polyamorphism of fullerite-based orientational glasses. https://doi.org/10.1063/1.1925371

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Deep-layered machines have a built-in Occam's razor

Many complex systems map vast numbers of microscopic descriptions onto far fewer macroscopic outcomes. Surprisingly, these input-output maps strongly favor simple outputs. In an exactly solvable deep-layered machine, we prove that simplicity bias strengthens with every layer. The probability of an output function $F$ approaches $\Pr(F)\propto 2^{-K(F)}$, where $K$ is the Kolmogorov complexity. Our results show that hierarchical composition alone can generate an intrinsic Occam's razor, suggesting a general mechanism for simplicity bias in learning and evolution.

cond-mat.dis-nn↗

arXiv2026

Neural Networks with Exciton-Polariton Waveguides

We propose and numerically study a scheme for neural networks using exciton-polariton waveguide meshes. These enable arbitrary programmable interlayer couplings suited to a restricted Boltzmann machine architecture where polariton phases act as stochastic network variables. We show that such a network can be trained using unsupervised Hebbian and anti-Hebbian updates. This allows the implementation of an associative memory, where the network recovers learned patterns from imperfect inputs.

cond-mat.dis-nn↗

arXiv2026

Finite-width shift of the edge of chaos in deep neural networks

Deep neural networks train best when they are initialised at the edge of chaos, the critical line in the plane of weight and bias variances on which the distance between two inputs neither grows nor shrinks with depth. The line is known for infinitely wide networks; at finite width it moves, and how it moves has been disputed. We treat the logarithm of the distance between two inputs through a network of width $N$ as a random walk and compute its drift and variance to order $1/N$. The edge of chaos moves to a larger weight variance, by $Δσ_w^2=-a_1/(N\,\partial_{σ_w^2}\lnχ_1)$, about $8/N$ for $\tanh$ networks at a bias variance of $0.05$, confirmed by simulations of the exact finite-width recursion. The shift describes a typical network; the average over many networks is dominated by rare networks in which the distance grows, and its critical point moves the other way, which explains why calculations and simulations have disagreed. Backpropagation follows the same random walk, so the shift applies to gradients unchanged. Along the edge of chaos, the information about the similarity of two inputs is constant until the compression over the depth exceeds about one nat, which fixes where to sit on the line. The results give an initialisation rule, Crinit, that places a network on its finite-width edge of chaos without a search. On MNIST and Fashion-MNIST at width $64$ it is more accurate than the standard rules at their usual settings, by a small margin in shallow networks and a large one in deep networks, where most standard rules fail; it trains in fewer steps, and it matches or beats the standard rules with a tuned gain.

cond-mat.dis-nn↗