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arXiv 2020年

作者： Röding, Magnus Ma, Zheng Torquato, Salvatore RISE Research Institutes of Sweden Göteborg41276 Sweden Department of Physics Princeton University PrincetonNJ08544 United States 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

Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to fac

关键词： Forecasting

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Extended virtual detector theory including quantum interferences

arXiv

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arXiv 2020年

作者： Xu, Rui-Hua Wang, Xu Eberly, J.H. Institute of Applied Physics and Computational Mathematics Beijing100094 China Graduate School China Academy of Engineering Physics Beijing100193 China Department of Physics and Astronomy University of Rochester RochesterNY14627 United States

We extend our earlier "virtual detector" method [X. Wang, J. Tian, and J. H. Eberly, Phys. Rev. Lett. 110, 243001 (2013)], a hybrid quantum mechanical and classical trajectory method, to include phases in the classical trajectories. Effects of quantum interferences, lost in the earlier method, are restored. The obtained photoelectron momentum distributions agree well with the corresponding numerical solutions of the time-dependent Schrödinger equation. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum interference devices

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Neural Canonical Transformation with Symplectic Flows

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Physical Review X 2020年第2期10卷 021020-021020页

作者： Shuo-Hui Li Chen-Xiao Dong Linfeng Zhang Lei Wang Institute of Physics Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Songshan Lake Materials Laboratory Dongguan Guangdong 523808 China

Canonical transformation plays a fundamental role in simplifying and solving classical Hamiltonian systems. Intriguingly, it has a natural correspondence to normalizing flows with a symplectic constraint. Building on this key insight, we design a neural canonical transformation approach to automatically identify independent slow collective variables in general physical systems and natural datasets. We present an efficient implementation of symplectic neural coordinate transformations and two ways to train the model based either on the Hamiltonian function or phase-space samples. The learned model maps physical variables onto an independent representation where collective modes with different frequencies are separated, which can be useful for various downstream tasks such as compression, prediction, control, and sampling. We demonstrate the ability of this method first by analyzing toy problems and then by applying it to real-world problems, such as identifying and interpolating slow collective modes of the alanine dipeptide molecule and MNIST database images.

关键词： Collective dynamics Hamiltonian systems Artificial neural networks Molecular dynamics

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Certified randomness using a trapped-ion quantum processor

arXiv

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arXiv 2025年

作者： Liu, Minzhao Shaydulin, Ruslan Niroula, Pradeep DeCross, Matthew Hung, Shih-Han Kon, Wen Yu Cervero-Martín, Enrique Chakraborty, Kaushik Amer, Omar Aaronson, Scott Acharya, Atithi Alexeev, Yuri Berg, K. Jordan Chakrabarti, Shouvanik Curchod, Florian J. Dreiling, Joan M. Erickson, Neal Foltz, Cameron Foss-Feig, Michael Hayes, David Humble, Travis S. Kumar, Niraj Larson, Jeffrey Lykov, Danylo Mills, Michael Moses, Steven A. Neyenhuis, Brian Eloul, Shaltiel Siegfried, Peter Walker, James Lim, Charles Pistoia, Marco Global Technology Applied Research JPMorganChase New YorkNY10017 United States Quantinuum BroomfieldCO80021 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Department of Physics The University of Chicago ChicagoIL60637 United States Department of Computer Science The University of Texas at Austin AustinTX78712 United States Department of Electrical Engineering National Taiwan University Taipei City10617 Taiwan Quantinuum Terrington House 13–15 Hills Road CambridgeCB2 1NL United Kingdom Quantum Science Center Oak Ridge National Laboratory Oak RidgeTN37831 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers [1, 2], realizing this potential remains a challenge. One such task is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy [3]. Certified randomness has many applications [4] but is fundamentally impossible to achieve solely by classical computation. In this work, we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations [5, 6]: a client generates quantum "challenge" circuits using a small randomness seed, sends them to an untrusted quantum server to execute, and verifies the server’s results. We analyze the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 1018 floating-point operations per second across multiple supercomputers, we certify 71, 313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of today’s quantum computers. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum computers

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Quantum initial conditions for curved inflating universes

arXiv

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arXiv 2022年

作者： Letey, Mary I. Shumaylov, Zakhar Agocs, Fruzsina J. Handley, Will J. Hobson, Michael P. Lasenby, Anthony N. Kavli Institute for Cosmology Madingley Road CambridgeCB3 0HA United Kingdom John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Center for Computational Mathematics Flatiron Institute 162 Fifth Avenue New YorkNY United States Astrophysics Group Cavendish Laboratory J. J. Thomson Avenue CambridgeCB3 0HE United Kingdom

We discuss the challenges of motivating, constructing, and quantising a canonically-normalised inflationary perturbation in spatially curved universes. We show that this has historically proved challenging due to the interaction of nonadiabaticity with spatial curvature. We construct a novel curvature perturbation that is canonically normalised in the sense of its equation of motion, and is unique up to a single scalar parameter. With this construction it becomes possible to set initial conditions invariant under canonical transformations, overcoming known ambiguities in the literature. This corrected quantisation has potentially observational consequences via modifications to the primordial power spectrum at large angular scales, as well as theoretical implications for quantisation procedures in curved cosmologies filled with a scalar field. Copyright © 2022, The Authors. All rights reserved.

关键词： Relativity

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Quantum embedding methods for correlated excited states of point defects: Case studies and challenges

arXiv

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arXiv 2021年

作者： Muechler, Lukas Badrtdinov, Danis I. Hampel, Alexander Cano, Jennifer Rösner, Malte Dreyer, Cyrus E. Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Theoretical Physics and Applied Mathematics Department Ural Federal University Yekaterinburg620002 Russia Department of Physics and Astronomy Stony Brook University Stony Brook NY11794-3800 United States Radboud University Institute for Molecules and Materials Heijendaalseweg 135 Nijmegen 6525 AJ Netherlands

A quantitative description of the excited electronic states of point defects and impurities is crucial for understanding materials properties, and possible applications of defects in quantum technologies. This is a considerable challenge for computational methods, since Kohn-Sham density-functional theory (DFT) is inherently a ground state theory, while higher-level methods are often too computationally expensive for defect systems. Recently, embedding approaches have been applied that treat defect states with many-body methods, while using DFT to describe the bulk host material. We implement such an embedding method, based on Wannierization of defect orbitals and the constrained random-phase approximation approach, and perform systematic characterization of the method for three distinct systems with current technological relevance: a carbon dimer replacing a B and N pair in bulk hexagonal BN (CBCN), the negatively charged nitrogen-vacancy center in diamond (NV−), and an Fe impurity on the Al site in wurtzite AlN (FeAl). For CBCN we show that the embedding approach gives many-body states in agreement with analytical results on the Hubbard dimer model, which allows us to elucidate the effects of the DFT functional and double-counting correction. For the NV− center, our method demonstrates good quantitative agreement with experiments for the zero-phonon line of the triplet-triplet transition. Finally, we illustrate challenges associated with this method for determining the energies and orderings of the complex spin multiplets in FeAl © 2021, CC BY.

关键词： Density functional theory

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Protein Folding as a Jamming Transition

PRX Life

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PRX Life 2025年第1期3卷 013018-013018页

作者： Alex T. Grigas Zhuoyi Liu Jack A. Logan Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Biomedical Informatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Proteins fold to a specific functional conformation with a densely packed core that controls their stability. Despite their importance, we lack a quantitative explanation for why all protein cores, regardless of their overall fold, possess the same average packing fraction 〈ϕ〉≈0.55. However, important developments in the physics of jamming in particulate systems can shed light on the packing of protein cores. Here, we extend the framework of jamming to describe core packing in collapsed polymers, as well as in all-atom models of folded proteins. First, we show in a spherical bead-spring polymer model (with and without bond-angle constraints) that as the hydrophobic interactions increase relative to thermal fluctuations, a jamming-like transition occurs when the core packing fraction exceeds ϕc with the same power-law scaling behavior for the potential energy Vr, excess contact number ΔN, and characteristic frequency of the vibrational density of states ω* versus Δϕ=ϕ−ϕc as that for jammed particulate systems. Then, we develop an all-atom model for proteins and find that, above ϕc∼0.55, protein cores undergo a jamming-like transition, but with anomalous power-law scaling for Vr, ΔN, and ω* versus Δϕ. The all-atom protein model remains close to the native protein structure during jamming and accurately refolds from partially unfolded states.

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Energy-positive soaring using transient turbulent fluctuations

arXiv

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arXiv 2023年

作者： He, Danyun Reddy, Gautam Rycroft, Chris H. John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Mathematics University of Wisconsin–Madison MadisonWI53706 United States Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States Center for Brain Science Harvard University CambridgeMA02138 United States NSF-Simons Center for Mathematical & Statistical Analysis of Biology Harvard University CambridgeMA02138 United States Computational Research Division Lawrence Berkeley Laboratory BerkeleyCA94720 United States

Soaring birds gain energy from stable ascending currents or shear. However, it remains unclear whether energy loss due to drag can be overcome by extracting work from transient turbulent fluctuations. We designed numerical simulations of gliders navigating in a kinematic model that captures the spatio-temporal correlations of atmospheric turbulence. Energy extraction is enabled by an adaptive algorithm based on Monte Carlo tree search that dynamically filters acquired information about the flow to plan future paths. We show that net energy gain is feasible under realistic constraints. Glider paths reflect patterns of foraging, where exploration of the flow is interspersed with bouts of energy extraction through localized spirals. © 2023, CC BY-NC-ND.

关键词： Shear flow

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Memory-based model of drug release from Metformin Hydrochloride/Vildagliptin tablets

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AIP Conference Proceedings 2024年第1期3094卷

作者： M. Khalaf A. Elsaid S. F. Hammad W. K. Zahra Department of Applied and Computational Mathematics Institute of Basic and Applied Sciences Egypt-Japan University of Science and Technology (E-JUST) New Borg El-Arab City Alexandria 21934 Egypt Basic Engineering Sciences Department Benha Faculty of Engineering Benha University Benha 13512 Egypt Department of Engineering Mathematics and Physics Faculty of Engineering Mansoura University Mansoura 35516 Egypt Institute of Basic and Applied Sciences Egypt-Japan University of Science and Technology (E-JUST) New Borg El-Arab City Alexandria 21934 Egypt Pharmaceutical Chemistry Department Faculty of Pharmacy Helwan University Ain Helwan 11795 Cairo Egypt Department of Engineering Physics and Mathematics Faculty of Engineering Tanta University Tanta 31521 Egypt

Memory effects are needed to be described in the diffiusion process modelling since there is inconsistent rate with the classical approach. In this paper, a mathematical model is presented for simulation drug release from Metformin Hydrochlo-ride/Vildagliptin tablets based on fractional derivative (FD) and memory-dependent derivative (MDD). Our problem is represented by a set of two numerically solved diffusion equations. For both the tablet and the release medium, the profiles of drug concentration and mass are given and compared to the in vivo profiles. Results indicate that the FD and MDD are better suited for temporal modelling.

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The First International Conference on Advances in computational Science and Engineering (ICACSE 2020)

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Journal of physics: Conference Series 2021年第1期1964卷

作者： Loganathan, K. Mohanraj, M. Sakthivel, P. Sivakumar, M. Research and Development Wing Tamil Nadu Tiruppur638 106 India Department of Mechanical Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore641 032 India Department of Physics Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore641 021 India Department of Applied Mathematics and Computational Science PSG College of Technology Tamil Nadu Coimbatore641 004 India

The First International Conference on Advances in computational Science and Engineering (ICACSE 2020) on December 25 & 26th, 2020 which is planned to be held at Coimbatore, India. As the covid-19 pandemic spreads endless, according to the direction received from the government and suggestions received from some participants, the organizing committee decided to convert ICACSE 2020 into a fully virtual mode conference. ICACSE 2020 conference was organized by the Live4Research Institute of Research and Development, India. The main objective of the conference is to provide a platform for researchers, professors, and engineers to share their contributions to recent trends and developments in Engineering and Sciences. The scientific event brings together more than 300 researchers. Initially, the conference structure was divided into oral presentations and keynote speeches. In the oral presentations, some scholars' papers were selected as excellent papers, were given about 5-10 minutes to present their oral presentations one by one. Then the keynote speakers were each allocated 30-45 minutes for their plenary talks. We have received tremendous response from all over India and overseas (Malaysia, Oman, Ethiopia, Peru, Columbia, USA). Through a rigorous peer-review process, all submissions were performed double blind review to check their quality of content, level of innovation, significance, originality, and readability. Based on the expertise review comments, only 295 papers were accepted, and the corresponding authors were invited to submit their papers for final publication. The accepted papers are published by IOP Conference Series “Journal of physics: Conference Series (JPCS)”. The conference proceeding is a compilation of the accepted papers and represents an interesting outcome of the conference. This proceeding covers six chapters: 1. Advances in computational Mathematical Sciences 2. Advances in computational physics and Material Sciences 3. Advances in Computer Sc

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