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Quantum simulation of materials at micron scales and beyond

Physical Review B 2008年第5期78卷 054118-054118页

作者： Qing Peng Xu Zhang Linda Hung Emily A. Carter Gang Lu Department of Physics and Astronomy California State University Northridge Northridge California USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey USA Department of Mechanical and Aerospace Engineering Princeton University Princeton New Jersey USA

We present a multiscale modeling approach that can simulate multimillion atoms effectively via density-functional theory. The method is based on the framework of the quasicontinuum (QC) approach with orbital-free density-functional theory (OFDFT) as its sole energetics formulation. The local QC part is formulated by the Cauchy-Born hypothesis with OFDFT calculations for strain energy and stress. The nonlocal QC part is treated by an OFDFT-based embedding approach, which couples OFDFT nonlocal atoms to local region atoms. The method—QCDFT—is applied to a nanoindentation study of an Al thin film, and the results are compared to a conventional QC approach. The results suggest that QCDFT represents a new direction for the quantum simulation of materials at length scales that are relevant to experiments.

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End-To-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics

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Physical Review applied 2020年第6期14卷 064020-064020页

作者： Re-Bing Wu Xi Cao Pinchen Xie Yu-xi Liu Department of Automation Tsinghua University Beijing 100084 China Beijing National Research Center for Information Science and Technology Beijing 100084 China Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Institute of Microelectronics Tsinghua University Beijing 100084 China

To achieve quantum machine learning on imperfect noisy intermediate-scale quantum (NISQ) processors, the entire physical implementation should include as few as possible hand-designed modules with only a few ad hoc parameters to be determined. This work presents such a hardware-friendly end-to-end quantum-machine-learning scheme that can be implemented with imperfect NISQ processors. The proposal transforms the machine-learning task to the optimization of controlled quantum dynamics, in which the learning model is parameterized by experimentally tunable control variables. Our design also enables automated feature selection by encoding the raw input to quantum states through agent control variables. Comparing with the gate-based parameterized quantum circuits, the proposed end-to-end quantum-learning model is easy to implement as there are only a few ad hoc parameters to be determined. Numerical simulations on the benchmarking MNIST (Mixed National Institute of Standards and Technology) dataset of handwritten digits demonstrate that the model can achieve high performance using only 3–5 qubits without downsizing the dataset, which shows great potential for accomplishing large-scale real-world learning tasks on NISQ processors.

关键词： Machine learning Noise Quantum control Quantum information architectures & platforms Artificial neural networks

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Dynamical Sampling with Random Noise

Dynamical Sampling with Random Noise

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International Conference on Sampling Theory and Applications

作者： Akram Aldroubi Longxiu Huang Ilya Krishtal Roy Lederman Department of Mathematics Vanderbilt University Department of Mathematics Northern Illinois University Program in Applied and Computational Mathematics Princeton University

ISBN: (纸本)9781538615669

In this paper we consider a system of dynamical sampling, i.e. sampling a signal f that evolves in time under the action of an evolution operator A. We discuss the error in the recovery of the original signal when the samples are corrupted by additive, independent and identically distributed (i.i.d) noise. We focus on the study of the mean squared error E(‖ε_n‖_2~2) between the original signal and the reconstructed signal obtained by solving a least squares problem. In the theoretical part, we give a formula for E(‖ε_n‖_2~2) and prove that E(‖ε_n‖_2~2) decreases as the number of the samples increases. In addition, we discuss several numerical experiments that verify the theoretical results.

关键词： original signal random noise time action formula Identically distributed Mean square error signals

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Beta expansions: a new approach to digitally corrected A/D conversion

Beta expansions: a new approach to digitally corrected A/D c...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： I. Daubechies R. DeVore C.S. Gunturk V.A. Vaishampayan Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Mathematics University of South Carolina Columbia SC USA Department of Mathematics Courant Institute of Mathematical Sciences New York NY USA Information Sciences Research AT and T Research Laboratories Florham Park NJ USA

We introduce a new architecture for pipelined (and also algorithmic) A/D converters that give exponentially accurate conversion using inaccurate comparators. An error analysis of a sigma-delta converter with an imperfect comparator and a constant input reveals a self-correction property that is not inherited by the successive refinement quantization algorithm that underlies both pipelined multistage A/D converters and algorithmic A/D converters. Motivated by this example, we introduce a new A/D converter, the beta converter, which has the same self-correction property as a sigma-delta converter but which exhibits higher order (exponential) accuracy with respect to the bit rate as compared to a sigma-delta converter, which exhibits only polynomial accuracy.

关键词： mathematics Delta-sigma modulation Error analysis Quantization Redundancy Error correction Voltage Sampling methods Fourier transforms Computer architecture

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Accurate deep potential model for the Al-Cu-Mg alloy in the full concentration space

arXiv

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arXiv 2020年

作者： Jiang, Wanrun Zhang, Yuzhi Zhang, Linfeng Wang, Han Songshan Lake Materials Laboratory DongguanGuangdong523808 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Beijing Institute of Big Data Research Beijing100871 China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China

Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher's stone for unraveling the nature of matter via atomistic simulation. This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES. In this work, we develop an accurate PES model for the Al-Cu-Mg system by employing Deep Potential (DP), a neural network based representation of the PES, and DP Generator (DP-GEN), a concurrent-learning scheme that generates a compact set of ab initio data for training. The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties, including formation energy, equilibrium volume, equation of state, interstitial energy, vacancy and surface formation energy, as well as elastic moduli. Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration. Copyright © 2020, The Authors. All rights reserved.

关键词： Copper alloys

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Difficulty in predicting shallow defects with hybrid functionals: Implication of the long-range exchange interaction

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Physical Review B 2013年第3期88卷 035134-035134页

作者： Junhyeok Bang Y. Y. Sun Tesfaye A. Abtew Amit Samanta Peihong Zhang S. B. Zhang Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy New York 12180 USA Department of Physics University at Buffalo State University of New York Buffalo New York 14260 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08540 USA

We identify an important issue in defect studies using hybrid functionals. When modeling a defect, which is supposedly an isolated system, with a finite-size supercell, the inclusion of a fraction of the Hartree-Fock interaction results in a strong cell-size dependence and an extremely slow convergence of the calculated defect properties, especially for shallow defect. These behaviors may give rise to a number of errors in calculated defect properties, including the deepening of transition level and overstabilization of shallow defects. Numerical results from hybrid functional calculations for a diverse array of systems can be understood within the Hartree-Fock theory of an electron-gas model, indicating that the long-range exchange is the main cause for the errors in the calculated defect properties within hybrid functionals.

关键词： EXCHANGE interactions (Magnetism) HARTREE-Fock approximation ELECTRON gas MATHEMATICAL models FUNCTIONS (mathematics) ERRORS

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Edge-Stress-Induced Warping of Graphene Sheets and Nanoribbons

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Physical Review Letters 2008年第24期101卷 245501-245501页

作者： V. B. Shenoy C. D. Reddy A. Ramasubramaniam Y. W. Zhang Division of Engineering Brown University Providence Rhode Island 02912 USA Institute of High Performance Computing Singapore 138632 Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Department of Materials Science and Engineering National University of Singapore Singapore 119260

We show that edge stresses introduce intrinsic ripples in freestanding graphene sheets even in the absence of any thermal effects. Compressive edge stresses along zigzag and armchair edges of the sheet cause out-of-plane warping to attain several degenerate mode shapes. Based on elastic plate theory, we identify scaling laws for the amplitude and penetration depth of edge ripples as a function of wavelength. We also demonstrate that edge stresses can lead to twisting and scrolling of nanoribbons as seen in experiments. Our results underscore the importance of accounting for edge stresses in thermal theories and electronic structure calculations for freestanding graphene sheets.

关键词： Nanoribbons

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Density of States for a Specified Correlation Function and the Energy Landscape

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Physical Review Letters 2012年第8期108卷 080601-080601页

作者： C. J. Gommes Y. Jiao S. Torquato Department of Chemical Engineering University of Liège B-4000 Liège Belgium Department of Chemistry Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics and Princeton Center for Theoretical Science Princeton University Princeton New Jersey 08544 USA

The degeneracy of two-phase disordered microstructures consistent with a specified correlation function is analyzed by mapping it to a ground-state degeneracy. We determine for the first time the associated density of states via a Monte Carlo algorithm. Our results are explained in terms of the roughness of an energy landscape, defined on a hypercubic configuration space. The use of a Hamming distance in this space enables us to define a roughness metric, which is calculated from the correlation function alone and related quantitatively to the structural degeneracy. This relation is validated for a wide variety of disordered structures.

关键词： ENERGY levels (Quantum mechanics) CORRELATION (Statistics) MICROSTRUCTURE GROUND state (Quantum mechanics) MONTE Carlo method CONFIGURATION space QUANTITATIVE research

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Jammed hard-particle packings: From Kepler to Bernal and beyond

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Reviews of Modern Physics 2010年第3期82卷 2633-2633页

作者： S. Torquato F. H. Stillinger []Department of Chemistry Department of Physics Princeton Center for Theoretical Science Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA and School of Natural Sciences Institute of Advanced Study Princeton New Jersey 08540 USA

Understanding the characteristics of jammed particle packings provides basic insights into the structure and bulk properties of crystals, glasses, and granular media and into selected aspects of biological systems. This review describes the diversity of jammed configurations attainable by frictionless convex nonoverlapping (hard) particles in Euclidean spaces and for that purpose it stresses individual-packing geometric analysis. A fundamental feature of that diversity is the necessity to classify individual jammed configurations according to whether they are locally, collectively, or strictly jammed. Each of these categories contains a multitude of jammed configurations spanning a wide and (in the large system limit) continuous range of intensive properties, including packing fraction ϕ, mean contact number Z, and several scalar order metrics. Application of these analytical tools to spheres in three dimensions (an analog to the venerable Ising model) covers a myriad of jammed states, including maximally dense packings (as Kepler conjectured), low-density strictly jammed tunneled crystals, and a substantial family of amorphous packings. With respect to the last of these, the current approach displaces the historically prominent but ambiguous idea of “random close packing” with the precise concept of “maximally random jamming.” Both laboratory procedures and numerical simulation protocols can and, frequently, have been used for creation of ensembles of jammed states. But while the resulting distributions of intensive properties may individually approach narrow distributions in the large system limit, the distinguishing varieties of possible operational details in these procedures and protocols lead to substantial variability among the resulting distributions, some examples of which are presented here. This review also covers recent advances in understanding jammed packings of polydisperse sphere mixtures, as well as convex nonspherical particles, e.g., ellipsoids, “

关键词： Spheres Euclidean space laboratory procedure jammed state Kepler

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DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

arXiv

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arXiv 2022年

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications. © 2022, CC BY.

关键词： Density functional theory

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