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Hyperuniformity classes of quasiperiodic tilings via diffusion spreadability

Physical Review E 2024年第6期109卷 064108-064108页

作者： Adam Hitin-Bialus Charles Emmett Maher Paul J. Steinhardt Salvatore Torquato Department of Physics <a href="https://***/00hx57361">Princeton University</a> Princeton New Jersey 08544 USA. Department of Chemistry <a href="https://***/00hx57361">Princeton University</a> Princeton New Jersey 08544 USA. Jefferson Physical Laboratory <a href="https://***/03vek6s52">Harvard University</a> Cambridge Massachusetts 02138 USA and Department of Physics <a href="https://***/00hx57361">Princeton University</a> Princeton New Jersey 08544 USA. Department of Chemistry <a href="https://***/00hx57361">Princeton University</a> Princeton New Jersey 08544 USA Department of Physics <a href="https://***/00hx57361">Princeton University</a> Princeton New Jersey 08544 USA Princeton Institute of Materials <a href="https://***/00hx57361">Princeton University</a> Princeton New Jersey 08544 USA and Program in Applied and Computational Mathematics <a href="https://***/00hx57361">Princeton University</a> Princeton New Jersey 08544 USA.

Hyperuniform point patterns can be classified by the hyperuniformity scaling exponent α>0, that characterizes the power-law scaling behavior of the structure factor S(k) as a function of wave number k≡|k| in the vicinity of the origin, e.g., S(k)∼|k|α in cases where S(k) varies continuously with k as k→0. In this paper, we show that the spreadability is an effective method for determining α for quasiperiodic systems where S(k) is discontinuous and consists of a dense set of Bragg peaks. It has been shown in [Phys. Rev. E 104, 054102 (2021)] that, for media with finite α, the long-time behavior of the excess spreadability S(∞)−S(t) can be fit to a power law of the form t−(d−α)/2, where d is the space dimension, to accurately extract α for the continuous case. We first transform quasiperiodic and limit-periodic point patterns into two-phase media by mapping them onto packings of identical nonoverlapping disks, where space interior to the disks represents one phase and the space in exterior to them represents the second phase. We then compute the spectral density χ̃V(k) of the packings, and finally compute and fit the long-time behavior of their excess spreadabilities. Specifically, we show that the excess spreadability can be used to accurately extract α for the one-dimensional (1D) limit-periodic period-doubling chain (α=1) and the 1D quasicrystalline Fibonacci chain (α=3) to within 0.02% of the analytically known exact results. Moreover, we obtain a value of α=5.97±0.06 for the two-dimensional Penrose tiling and present plausible theoretical arguments strongly suggesting that α is exactly equal to six. We also show that, due to the self-similarity of the structures examined here, one can truncate the small-k region of the scattering information used to compute the spreadability and obtain an accurate value of α, with a small deviation from the untruncated case that decreases as the system size increases. This strongly suggests that one can obtain a good estimate of

关键词： Quasicrystals

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Machine learning based non-Newtonian fluid model with molecular fidelity

arXiv

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arXiv 2020年

作者： Lei, Huan Wu, Lei Weinan, E. Department of Computational Mathematics Science & Engineering Department of Statistics & Probability Michigan State University MI48824 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States

We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Polymer solution is used as an example to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the micro-scale polymer configurations and their macro-scale counterparts, a set of nonlinear conformation tensors. The dynamics of these conformation tensors can be derived from the micro-scale model and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a new form of the objective tensor derivative. Numerical results demonstrate the accuracy of DeePN2. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscous flow

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Realizability of Iso-g2 Processes via Effective Pair Interactions

arXiv

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arXiv 2022年

作者： Wang, Haina Stillinger, Frank H. Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute of Materials and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States

An outstanding problem in statistical mechanics is the determination of whether prescribed functional forms of the pair correlation function g2(r) [or equivalently, structure factor S(k)] at some number density ρ can be achieved by d-dimensional many-body systems. The Zhang-Torquato conjecture states that any realizable set of pair statistics, whether from a nonequilibrium or equilibrium system, can be achieved by equilibrium systems involving up to two-body interactions. In light of this conjecture, we study the realizability problem of the nonequilibrium iso-g2 process, i.e., the determination of density-dependent effective potentials that yield equilibrium states in which g2 remains invariant for a positive range of densities. Using a precise inverse methodology that determines effective potentials that match hypothesized functional forms of g2(r) for all r and S(k) for all k, we show that the unit-step function g2, which is the zero-density limit of the hard-sphere potential, is remarkably numerically realizable up to the packing fraction = 0.49 for d = 1. For d = 2 and 3, it is realizable up to the maximum "terminal" packing fraction c = 1/2d, at which the systems are hyperuniform, implying that the explicitly known necessary conditions for realizability are sufficient up through c. For near but below c, the large-r behaviors of the effective potentials are given exactly by the functional forms exp[−κ()r] for d = 1, r−1/2 exp[−κ()r] for d = 2, and r−1 exp[−κ()r] (Yukawa form) for d = 3, where κ−1() is a screening length, and for = c, the potentials at large r are given by the pure Coulomb forms in the respective dimensions, as predicted by Torquato and Stillinger [Phys. Rev. E, 68, 041113 1-25 (2003)]. We also find that the effective potential for the pair statistics of the 3D "ghost" random sequential addition at the saturation density c = 1/8 is much shorter-ranged than that for the 3D unit-step-function g2 at c, and thus does not constrain the realizability

关键词： Inverse problems

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Liquid-Liquid Transition in Water from First Principles

引用

Physical Review Letters 2022年第25期129卷 255702-255702页

作者： Thomas E. Gartner, III Pablo M. Piaggi Roberto Car Athanassios Z. Panagiotopoulos Pablo G. Debenedetti Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA Department of Chemical and Biological Engineering Princeton University Princeton New Jersey 08544 USA

A long-standing question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. We used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles.

关键词： Classical statistical mechanics First order phase transitions First-principles calculations Liquid-liquid phase transition Phase transitions Thermodynamics Deep learning Density functional theory Molecular dynamics

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Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order-disorder transitions in LixCoO2

arXiv

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arXiv 2023年

作者： Teichert, G.H. Das, S. Shojaei, M. Faghih Holber, J. Mueller, T. Hung, L. Gavini, V. Garikipati, K. Department of Mechanical Engineering University of Michigan United States Toyota Research Institute Los AltosCA United States Department of Materials Science & Engineering University of Michigan United States Department of Mathematics University of Michigan United States Michigan Institute for Computational Discovery & Engineering University of Michigan United States Applied Physics Program University of Michigan United States Materials Science & Engineering Johns Hopkins University United States

LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for Li-ion batteries, whose performance is strongly governed by Li composition-dependent crystal structure and phase stability. Here, we use LixCoO2 (LCO) as a model system to benchmark a machine learning-enabled framework for bridging scales in materials physics. We focus on two scales: (a) assemblies of thousands of atoms described by density functional theory-informed statistical mechanics, and (b) continuum phase field models to study the dynamics of order-disorder transitions in LCO. Central to the scale bridging is the rigorous, quantitatively accurate, representation of the free energy density and chemical potentials of this material system by coarsegraining formation energies for specific atomic configurations. We develop active learning workflows to train recently developed integrable deep neural networks for such high-dimensional free energy density and chemical potential functions. The resulting, first principles-informed, machine learning-enabled, phase-field computations allow us to study LCO cathodes’ phase evolution in terms of temperature, morphology, charge cycling and particle size. © 2023, CC BY-NC-SA.

关键词： Cathodes

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Integrability of Goldilocks quantum cellular automata

arXiv

引用

arXiv 2024年

作者： Hillberry, Logan E. Piroli, Lorenzo Vernier, Eric Halpern, Nicole Yunger Prosen, Tomaž Carr, Lincoln D. Department of Physics The University of Texas at Austin AustinTX78712 United States Dipartimento di Fisica e Astronomia Università di Bologna INFN Sezione di Bologna via Irnerio 46 BolognaI-40126 Italy Laboratoire de Probabilités Statistique et Modélisation CNRS Université Paris Cité Sorbonne Université Paris France Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Faculty of Mathematics and Physics University of Ljubljana Jadranska 19 LjubljanaSI-1000 Slovenia Institute of Mathematics Physics and Mechanics Jadranska 19 LjubljanaSI-1000 Slovenia Quantum Engineering Program Colorado School of Mines GoldenCO80401 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

Goldilocks quantum cellular automata (QCA) have been simulated on quantum hardware and produce emergent small-world correlation networks. In Goldilocks QCA, a single-qubit unitary is applied to each qubit in a one-dimensional chain subject to a balance constraint: a qubit is updated if its neighbors are in opposite basis states. Here, we prove that a subclass of Goldilocks QCA—including the one implemented experimentally—map onto free fermions and therefore can be classically simulated efficiently. We support this claim with two independent proofs, one involving a Jordan–Wigner transformation and one mapping the integrable six-vertex model to QCA. We compute local conserved quantities of these QCA and predict experimentally measurable expectation values. These calculations can be applied to test large digital quantum computers against known solutions. In contrast, typical Goldilocks QCA have equilibration properties and quasienergy-level statistics that suggest nonintegrability. Still, the latter QCA conserve one quantity useful for error mitigation. Our work provides a parametric quantum circuit with tunable integrability properties with which to test quantum hardware. © 2024, CC BY.

关键词： Qubits

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Emulating Complex Synapses Using Interlinked Proton Conductors

arXiv

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arXiv 2024年

作者： Zhang, Lifu Li, Ji-An Hu, Yang Jiang, Jie Lai, Rongjie Benna, Marcus K. Shi, Jian Department of Materials Science and Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Neurosciences Graduate Program University of California San Diego La Jolla CA92093 United States Department of Mathematics Purdue University West LafayetteIN47907 United States Department of Neurobiology School of Biological Sciences University of California at San Diego La Jolla CA92093 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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Local Order Metrics for Two-Phase Media Across Length Scales

arXiv

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arXiv 2022年

作者： Torquato, Salvatore Skolnick, Murray Kim, Jaeuk Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States

The capacity to devise order metrics for microstructures of multiphase heterogeneous media is a highly challenging task, given the richness of the possible geometries and topologies of the phases that can arise. This investigation initiates a program to formulate order metrics to characterize the degree of order/disorder of the microstructures of two-phase media in d-dimensional Euclidean space Rd across length scales. In particular, we propose the use of the local volume-fraction variance σV2 (R) associated with a spherical window of radius R as an order metric. We determine σV2 (R) as a function of R for 22 different models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. We find that the local volume-fraction variance as well as asymptotic coefficients and integral measures derived from it provide reasonably robust and sensitive order metrics to categorize disordered and ordered two-phase media across all length scales. © 2022, CC BY-SA.

关键词： Volume fraction

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Quantifying yeast colony morphologies with feature engineering from time-lapse photography

arXiv

引用

arXiv 2022年

作者： Goldschmidt, Andy Kunert-Graf, James Scott, Adrian C. Tan, Zhihao Dudley, Aimée M. Nathan Kutz, J. Department of Physics University of Washington SeattleWA98195 United States Pacific Northwest Research Institute SeattleWA98122 United States Molecular and Cellular Biology Graduate Program University of Washington SeattleWA98195 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States

Baker's yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we collect a dataset of time-lapse photographs of the growth of different strains of S. cerevisiae. We discuss the general statistical challenges that arise when using time-lapse photographs to extract time-dependent features. In particular, we show how texture-based feature engineering and representative clustering can be successfully applied to categorize the development of yeast colony morphology using our dataset. The Local binary patterns (LBP) from image processing is used to score the surface texture of colonies. This texture score develops along a smooth trajectory during growth. The path taken depends on how the morphology emerges. A hierarchical clustering of the colonies is performed according to their texture development trajectories. The clustering method is designed for practical interpretability;it obtains the best representative colony image for any hierarchical sub-cluster. Copyright © 2022, The Authors. All rights reserved.

关键词： Morphology

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C2-GaMe: Classification of Cluster Galaxy Membership with Machine Learning

arXiv

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arXiv 2022年

作者： Farid, Daniel Aung, Han Nagai, Daisuke Farahi, Arya Rozo, Eduardo Program in Applied Mathematics Yale University New HavenCT06511 United States Department of Physics Yale University New HavenCT06520 United States Department of Statistics and Data Science The University of Texas AustinTX78712 United States Department of Physics University of Arizona TucsonAZ85721 United States

We present Classification of Cluster Galaxy Members (C2-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using phase space information as input. We train and test C2-GaMe with the galaxies from UniverseMachine mock catalog based on Multi-Dark Planck 2 N-body simulations. We show that probabilistic classification is superior to deterministic classification in estimating the physical properties of clusters, including density profiles and velocity dispersion. We propose a set of estimators to get an unbiased estimation of cluster properties. We demonstrate that C2-GaMe can recover the distribution of orbiting and infalling galaxies' position and velocity distribution with Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

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