2024 · Physical Review Letters
Jonathan Colen, Alexis Poncet, Denis Bartolo, Vincenzo Vitelli
We present a data-driven pipeline for model building that combines interpretable machine learning, hydrodynamic theories, and microscopic models. The goal is to uncover the underlying processes governing nonlinear dynamics experiments. We exemplify our method with data from microfluidic experiments where crystals of streaming droplets support the propagation of nonlinear waves absent in passive crystals. By combining physics-inspired neural networks, known as neural operators, with symbolic regression tools, we infer the solution, as well as the mathematical form, of a nonlinear dynamical system that accurately models the experimental data. Finally, we interpret this continuum model from fundamental physics principles. Informed by machine learning, we coarse grain a microscopic model of interacting droplets and discover that nonreciprocal hydrodynamic interactions stabilize and promote nonlinear wave propagation.
6 citations2 viewsFull text
DOI: 10.1103/physrevlett.133.1073011987 · Physical Review Letters
Eli Yablonovitch
It has been recognized for some time that the spontaneous emission by atoms is not necessarily a fixed and immutable property of the coupling between matter and space, but that it can be controlled by modification of the properties of the radiation field. This is equally true in the solid state, where spontaneous emission plays a fundamental role in limiting the performance of semiconductor lasers, heterojunction bipolar transistors, and solar cells. If a three-dimensionally periodic dielectric structure has an electromagnetic band gap which overlaps the electronic band edge, then spontaneous emission can be rigorously forbidden.
13,997 citations4 viewsFull text
DOI: 10.1103/physrevlett.58.20591982 · Physical Review Letters
Leonard Kleinman, D. M. Bylander
A simple way has been discovered to put model pseudopotentials, $V(\stackrel{\ensuremath{\rightarrow}}{\mathrm{r}})={\ensuremath{\Sigma}}_{\mathrm{lm}}|{Y}_{\mathrm{lm}}〉{V}_{l}(r)\ifmmode\times\else\texttimes\fi{}〈{Y}_{\mathrm{lm}}|$, into a form which reduces the number of integrals of $V(\stackrel{\ensuremath{\rightarrow}}{\mathrm{r}})$ required for an energyband calculation from $\frac{\mathrm{mn}(n+1)}{2}$ to $\mathrm{mn}$ for each $l$ in the sum (where $n$ is the number of plane waves used in the expansion and $m$ the number of points in the Brillouin zone at which the calculation is performed). The new form may be chosen to improve the accuracy of the pseudopotential when used in other chemical environments.
5,683 citations3 views
DOI: 10.1103/physrevlett.48.14251975 · Physical Review Letters
David C. Sherrington, Scott Kirkpatrick
We consider an Ising model in which the spins are coupled by infinite-ranged random interactions independently distributed with a Gaussian probability density. Both "spinglass" and ferromagnetic phases occur. The competition between the phases and the type of order present in each are studied.
4,312 citations4 views
DOI: 10.1103/physrevlett.35.17921967 · Physical Review Letters
Steven Weinberg
Received 17 October 1967DOI:https://doi.org/10.1103/PhysRevLett.19.1264©1967 American Physical Society
7,323 citations1 viewsFull text
DOI: 10.1103/physrevlett.19.12641966 · Physical Review Letters
N. David Mermin, Hermann‐Josef Wagner
It is rigorously proved that at any nonzero temperature, a one- or two-dimensional isotropic spin-$S$ Heisenberg model with finite-range exchange interaction can be neither ferromagnetic nor antiferromagnetic. The method of proof is capable of excluding a variety of types of ordering in one and two dimensions.
8,303 citations5 views
DOI: 10.1103/physrevlett.17.1133