Showing 1–37 of 37 results for author: Jin, P

CSympNet-ID: conformal-symplectic map learning for linearly damped Hamiltonian systems

Authors: Jiale Gong, Pengzhan Jin, Dongyang Kuang, Lu Li, Yifa Tang

Abstract: Learning dissipative dynamics from discrete observations is essential for reliable long-horizon prediction and physically meaningful parameter identification. For linearly damped Hamiltonian systems, the exact flow is generally not symplectic but conformally symplectic, contracting the canonical symplectic form by a scalar factor that reflects the net dissipation. We propose Conformal Symplectic N… ▽ More Learning dissipative dynamics from discrete observations is essential for reliable long-horizon prediction and physically meaningful parameter identification. For linearly damped Hamiltonian systems, the exact flow is generally not symplectic but conformally symplectic, contracting the canonical symplectic form by a scalar factor that reflects the net dissipation. We propose Conformal Symplectic Networks with damping identification (CSympNet-ID), a discrete-time map-learning framework that learns the one-step flow map directly from snapshot pairs while enforcing exact discrete conformal symplecticity by construction, without penalty terms or projection. The architecture composes an exact symplectic neural core with explicit diagonal scaling layers whose factors are parameterized exponentially by a scalar damping-rate parameter, thereby guaranteeing positivity and interpretability of the learned dissipation factor. We establish a scaling-conjugacy factorization for conformal symplectic maps and derive a pointwise-in-step density result for CSympNet-ID. We evaluate an irregular-step damped oscillator, a damped spring-mass chain, a damped nonlinear cubic oscillator, and additional high-dimensional extensions. CSympNet-ID gives the most favorable overall results among the compared models in the reported experiments, particularly in data-scarce regimes, target contraction-law recovery, and high-dimensional tests where unstructured baselines degrade rapidly. △ Less

Submitted 3 July, 2026; originally announced July 2026.

Comments: 24 pages, 7 figures

MSC Class: G.1.0; I.2.6

A Pilot Kinematic Study on the Forehand Reverse Flick: Feasibility of a Novel Short Return Technique in Table Tennis

Authors: Pengfei Jin, Jie Ren, Chen Yang, Qingtao Kong, Qingshan Zhang, Nan Gu, Bin Chen, Qin Zhang, Zhe Feng

Abstract: Background Following changes in table tennis ball materials, offensive returns have become more important for initiating sustained topspin offense. However, using the backhand flick (BF) to return forehand short balls often increases the difficulty of recovery and continuity, revealing a technical gap. This study preliminarily verified a novel forehand short return technique, the forehand reverse… ▽ More Background Following changes in table tennis ball materials, offensive returns have become more important for initiating sustained topspin offense. However, using the backhand flick (BF) to return forehand short balls often increases the difficulty of recovery and continuity, revealing a technical gap. This study preliminarily verified a novel forehand short return technique, the forehand reverse flick (FRF), and analyzed its similarities and differences with the BF. Methods Four elite athletes completed seven consecutive days of FRF specific training. Infrared motion capture and ultra-high-speed cameras were used to collect data on racket kinematics, movement duration, and ball performance. Results The success rate of the FRF increased steadily, reaching 86%. Racket trajectories of the two techniques were highly similar along the X (r = 1) and Y (r = 0.99) axes but differed along the Z (r = -0.04) axis. Racket and ball velocities were comparable between techniques, whereas the FRF showed lower resultant acceleration (approximately 265.57 m/s) and required about 0.03 s more for movement duration. Ball velocity was comparable between techniques, for the ball spin, the FRF generated lower spin (approximately 76.61 r/s) about 64% of the BF value (approximately 120.13 r/s). The highest participant mean spin rate reached 93 r/s, about 77% of the BF mean. Conclusion Overall, the FRF was found to have favorable learnability and training value, with potential for further optimization and competitive application. △ Less

Submitted 12 January, 2026; originally announced January 2026.

Comments: 20pages, 7 Figure

Calibrating Geophysical Predictions under Constrained Probabilistic Distributions

Authors: Zhewen Hou, Jiajin Sun, Subashree Venkatasubramanian, Peter Jin, Shuolin Li, Tian Zheng

Abstract: Machine learning (ML) has shown significant promise in studying complex geophysical dynamical systems, including turbulence and climate processes. Such systems often display sensitive dependence on initial conditions, reflected in positive Lyapunov exponents, where even small perturbations in short-term forecasts can lead to large deviations in long-term outcomes. Thus, meaningful inference requir… ▽ More Machine learning (ML) has shown significant promise in studying complex geophysical dynamical systems, including turbulence and climate processes. Such systems often display sensitive dependence on initial conditions, reflected in positive Lyapunov exponents, where even small perturbations in short-term forecasts can lead to large deviations in long-term outcomes. Thus, meaningful inference requires not only accurate short-term predictions, but also consistency with the system's long-term attractor that is captured by the marginal distribution of state variables. Existing approaches attempt to address this challenge by incorporating spatial and temporal dependence, but these strategies become impractical when data are extremely sparse. In this work, we show that prior knowledge of marginal distributions offers valuable complementary information to short-term observations, motivating a distribution-informed learning framework. We introduce a calibration algorithm based on normalization and the Kernelized Stein Discrepancy (KSD) to enhance ML predictions. The method here employs KSD within a reproducing kernel Hilbert space to calibrate model outputs, improving their fidelity to known physical distributions. This not only sharpens pointwise predictions but also enforces consistency with non-local statistical structures rooted in physical principles. Through synthetic experiments-spanning offline climatological CO2 fluxes and online quasi-geostrophic flow simulations-we demonstrate the robustness and broad utility of the proposed framework. △ Less

Submitted 5 December, 2025; v1 submitted 28 November, 2025; originally announced December 2025.

Projection Embedded Diffusion Bridge for CT Reconstruction from Incomplete Data

Authors: Yuang Wang, Pengfei Jin, Siyeop Yoon, Matthew Tivnan, Shaoyang Zhang, Li Zhang, Quanzheng Li, Zhiqiang Chen, Dufan Wu

Abstract: Reconstructing CT images from incomplete projection data remains challenging due to the ill-posed nature of the problem. Diffusion bridge models have recently shown promise in restoring clean images from their corresponding Filtered Back Projection (FBP) reconstructions, but incorporating data consistency into these models remains largely underexplored. Incorporating data consistency can improve r… ▽ More Reconstructing CT images from incomplete projection data remains challenging due to the ill-posed nature of the problem. Diffusion bridge models have recently shown promise in restoring clean images from their corresponding Filtered Back Projection (FBP) reconstructions, but incorporating data consistency into these models remains largely underexplored. Incorporating data consistency can improve reconstruction fidelity by aligning the reconstructed image with the observed projection data, and can enhance detail recovery by integrating structural information contained in the projections. In this work, we propose the Projection Embedded Diffusion Bridge (PEDB). PEDB introduces a novel reverse stochastic differential equation (SDE) to sample from the distribution of clean images conditioned on both the FBP reconstruction and the incomplete projection data. By explicitly conditioning on the projection data in sampling the clean images, PEDB naturally incorporates data consistency. We embed the projection data into the score function of the reverse SDE. Under certain assumptions, we derive a tractable expression for the posterior score. In addition, we introduce a free parameter to control the level of stochasticity in the reverse process. We also design a discretization scheme for the reverse SDE to mitigate discretization error. Extensive experiments demonstrate that PEDB achieves strong performance in CT reconstruction from three types of incomplete data, including sparse-view, limited-angle, and truncated projections. For each of these types, PEDB outperforms evaluated state-of-the-art diffusion bridge models across standard, noisy, and domain-shift evaluations. △ Less

Submitted 26 October, 2025; originally announced October 2025.

Comments: 53 pages, 7 figures, submitted to Medical Image Analysis

Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies

Authors: Tian Zheng, Subashree Venkatasubramanian, Shuolin Li, Amy Braverman, Xinyi Ke, Zhewen Hou, Peter Jin, Samarth Sanjay Agrawal

Abstract: Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale coupling, data sparsity, robust generalization, and integration with scientific workflows. This paper analyzes a series of case studies from applied machine learnin… ▽ More Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale coupling, data sparsity, robust generalization, and integration with scientific workflows. This paper analyzes a series of case studies from applied machine learning research in climate modeling, with a focus on design choices and workflow structure. Rather than reviewing technical details, we aim to synthesize workflow design patterns across diverse projects in ML-enabled climate modeling: from surrogate modeling, ML parameterization, probabilistic programming, to simulation-based inference, and physics-informed transfer learning. We unpack how these workflows are grounded in physical knowledge, informed by simulation data, and designed to integrate observations. We aim to offer a framework for ensuring rigor in scientific machine learning through more transparent model development, critical evaluation, informed adaptation, and reproducibility, and to contribute to lowering the barrier for interdisciplinary collaboration at the interface of data science and climate modeling. △ Less

Submitted 30 September, 2025; originally announced October 2025.

Comments: Supplement

MSC Class: 62P12 62p12

OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography

Authors: Hanchen Wang, Yixuan Wu, Yinan Feng, Peng Jin, Luoyuan Zhang, Shihang Feng, James Wiskin, Baris Turkbey, Peter A. Pinto, Bradford J. Wood, Songting Luo, Yinpeng Chen, Emad Boctor, Youzuo Lin

Abstract: Prostate cancer is one of the most prevalent and deadly cancers among men, motivating the development of accurate and accessible imaging technologies for early detection. Ultrasound computed tomography (USCT) reconstructs quantitative tissue parameters such as speed-of-sound (SOS) and is a promising low-cost alternative to existing modalities. However, prostate USCT remains challenging due to limi… ▽ More Prostate cancer is one of the most prevalent and deadly cancers among men, motivating the development of accurate and accessible imaging technologies for early detection. Ultrasound computed tomography (USCT) reconstructs quantitative tissue parameters such as speed-of-sound (SOS) and is a promising low-cost alternative to existing modalities. However, prostate USCT remains challenging due to limited-angle acquisition, strong tissue heterogeneity, bone-induced wave distortion, and the lack of large-scale, anatomically realistic datasets for method development and evaluation. We introduce OPENPROS, the first large-scale benchmark dataset for limited-angle prostate USCT, designed to systematically evaluate machine learning methods for quantitative inverse problems. OPENPROS contains over 280,000 paired samples of realistic 2D SOS maps and corresponding ultrasound full-waveform data, generated from anatomically accurate 3D digital prostate models derived from 4 clinical MRI/CT scans and 62 ex vivo prostate specimens with experimental ultrasound measurements. Wave propagation is simulated under clinically realistic configurations using open-source finite-difference time-domain and Runge-Kutta solvers. We provide standardized training, in-distribution, and out-of-distribution benchmarks and evaluate representative deep learning baselines. While learning-based methods substantially improve inference speed and reconstruction accuracy over physics-based approaches, results highlight persistent challenges in robustness, generalization, and high-resolution reconstruction quality. By publicly releasing OPENPROS, we establish a rigorous benchmark to support research in inverse problems, physics-guided learning, and operator learning, and to bridge the gap between machine learning research and practical USCT deployment. The dataset is available at https://open-pros.github.io/. △ Less

Submitted 1 February, 2026; v1 submitted 18 May, 2025; originally announced May 2025.

Free-form intelligent hydrodynamic metamaterials enabled by extreme anisotropy

Authors: Fubao Yang, Yuhong Zhou, Peng Jin, Liujun Xu, Gaole Dai, Jiping Huang

Abstract: Intelligent metamaterials have attracted widespread research interest due to their self-adaptive capabilities and controllability. They hold great potential for advancing fluid control by providing responsive and flexible solutions. However, current designs of passive hydrodynamic metamaterials are limited by their fixed shapes and specific environments, lacking environmental adaptability. These t… ▽ More Intelligent metamaterials have attracted widespread research interest due to their self-adaptive capabilities and controllability. They hold great potential for advancing fluid control by providing responsive and flexible solutions. However, current designs of passive hydrodynamic metamaterials are limited by their fixed shapes and specific environments, lacking environmental adaptability. These two constraints hinder the broader application of hydrodynamic metamaterials. In this work, we propose a design for passive intelligent metashells that utilize extremely anisotropic parameters to endow hydrodynamic metamaterials with self-adaptive abilities and free-form shapes. Achieving the required anisotropic parameters is challenging, but we ingeniously accomplished this by creating isobaric conditions through increasing the water height in the shell region. We validated the design through finite-element simulations. This approach overcomes the limitations of existing passive hydrodynamic metamaterials, enhancing their intelligent behavior. Our model improves the flexibility and robustness of hydrodynamic metamaterials in complex and dynamic environments, providing insights for future designs and practical applications △ Less

Submitted 3 December, 2024; originally announced December 2024.

Invisible Hydrodynamic Tweezers Based on Near-Zero Index Materials

Authors: Yuhong Zhou, Fubao Yang, Jinrong Liu, Gaole Dai, Zixin Li, Xuzhi Zhou, Peng Jin, Jiping Huang

Abstract: Manipulating particles, such as cells and tissues, in a flowing liquid environment is crucial for life science research. Traditional contactless tweezers, although widely used for single-cell manipulation, face several challenges. These include potential damage to the target, restriction to static environments, complex excitation setups, and interference outside the target area. To address these i… ▽ More Manipulating particles, such as cells and tissues, in a flowing liquid environment is crucial for life science research. Traditional contactless tweezers, although widely used for single-cell manipulation, face several challenges. These include potential damage to the target, restriction to static environments, complex excitation setups, and interference outside the target area. To address these issues, we propose an ``invisible hydrodynamic tweezer'' utilizing near-zero index hydrodynamic metamaterials. This metamaterial-based device creates an equipotential resistance zone, effectively immobilizing particles in flowing fluids without disturbing the external flow field and without causing damage to the targets. Unlike traditional active control methods, our tweezer passively captures and releases particles by adjusting the flow channel, eliminating the need for continuous and stable excitation devices, thereby significantly simplifying the setup complexity. Furthermore, these tweezers can be modularly designed in different sizes to flexibly accommodate various application needs. Simulations and experimental validations demonstrated the non-interfering, stable trapping, and precise movement capabilities of these tweezers. This proposed technique holds significant potential for applications in biomedicine, microfluidics, and environmental monitoring. △ Less

Submitted 5 January, 2025; v1 submitted 28 November, 2024; originally announced December 2024.

Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Authors: Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu

Abstract: In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g.… ▽ More In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g. energy, so their data are often generated by cheaper computational methods at the cost of lower accuracy, which cannot be directly overcome through multi-task learning. Moreover, it is not straightforward to leverage abundant data of other tasks to benefit a particular task. To handle such data heterogeneity challenges, we exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches that allow different tasks to exchange information directly so as to improve one another. Particularly, we demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction, demonstrating a broad capability for integrating heterogeneous data. △ Less

Submitted 13 October, 2024; originally announced October 2024.

Comments: Published as a conference paper at NeurIPS 2024

Topological thermal transport

Authors: Zhoufei Liu, Peng Jin, Min Lei, Chengmeng Wang, Fabio Marchesoni, Jian-Hua Jiang, Jiping Huang

Abstract: Thermal transport is a fundamental mechanism of energy transfer process quite distinct from wave propagation phenomena. It can be manipulated well beyond the possibilities offered by natural materials with a new generation of artificial metamaterials: thermal metamaterials. Topological physics, a focal point in contemporary condensed matter physics, is closely intertwined with thermal metamaterial… ▽ More Thermal transport is a fundamental mechanism of energy transfer process quite distinct from wave propagation phenomena. It can be manipulated well beyond the possibilities offered by natural materials with a new generation of artificial metamaterials: thermal metamaterials. Topological physics, a focal point in contemporary condensed matter physics, is closely intertwined with thermal metamaterials in recent years. Inspired by topological photonics and topological acoustics in wave metamaterials, a new research field emerged recently, which we dub `topological thermotics', which encompasses three primary branches: topological thermal conduction, convection, and radiation. For topological thermal conduction, we discuss recent advances in both 1D and higher-dimensional thermal topological phases. For topological thermal convection, we discuss the implementation of thermal exceptional points with their unique properties and non-Hermitian thermal topological states. Finally, we review the most recent demonstration of topological effects in the near-field and far-field radiation. Anticipating future developments, we conclude by discussing potential directions of topological thermotics, including the expansion into other diffusion processes such as particle dynamics and plasma physics, and the integration with machine learning techniques. △ Less

Submitted 2 September, 2024; originally announced September 2024.

Comments: This perpective summarizes the topological physics in thermal metamaterials and proposes a new research field, "topological thermotics"

Journal ref: Nat. Rev. Phys. 6, 554-565 (2024)

Reconfigurable, zero-energy, and wide-temperature loss-assisted thermal nonreciprocal metamaterials

Authors: Min Lei, Peng Jin, Yuhong Zhou, Ying Li, Liujun Xu, Jiping Huang

Abstract: Thermal nonreciprocity plays a vital role in chip heat dissipation, energy-saving design, and high-temperature hyperthermia, typically realized through the use of advanced metamaterials with nonlinear, advective, spatiotemporal, or gradient properties. However, challenges such as fixed structural designs with limited adjustability, high energy consumption, and a narrow operational temperature rang… ▽ More Thermal nonreciprocity plays a vital role in chip heat dissipation, energy-saving design, and high-temperature hyperthermia, typically realized through the use of advanced metamaterials with nonlinear, advective, spatiotemporal, or gradient properties. However, challenges such as fixed structural designs with limited adjustability, high energy consumption, and a narrow operational temperature range remain prevalent. Here, a systematic framework is introduced to achieve reconfigurable, zero-energy, and wide-temperature thermal nonreciprocity by transforming wasteful heat loss into a valuable regulatory tool. Vertical slabs composed of natural bulk materials enable asymmetric heat loss through natural convection, disrupting the inversion symmetry of thermal conduction. The reconfigurability of this system stems from the ability to modify heat loss by adjusting thermal conductivity, size, placement, and quantity of the slabs. Moreover, this structure allows for precise control of zero-energy thermal nonreciprocity across a broad temperature spectrum, utilizing solely environmental temperature gradients without additional energy consumption. This research presents a different approach to achieving nonreciprocity, broadening the potential for nonreciprocal devices such as thermal diodes and topological edge states, and inspiring further exploration of nonreciprocity in other loss-based systems. △ Less

Submitted 25 October, 2024; v1 submitted 6 January, 2024; originally announced May 2024.

Comments: 23 pages, 4 figures

Journal ref: PNAS, volume 121, e2410041121 (2024)

A dynamic thermal sensing mechanism with reconfigurable expanded-plane structures

Authors: Haohan Tan, Haoyang Cai, Peng Jin, Jiping Huang

Abstract: The precise measurement of temperature is crucial in various fields such as biology, medicine, industrial automation, energy management, and daily life applications. While in most scenarios, sensors with a fixed thermal conductivity inevitably mismatch the analogous parameter of the medium being measured, thus causing the distortion and inaccurate detection of original temperature fields. Despite… ▽ More The precise measurement of temperature is crucial in various fields such as biology, medicine, industrial automation, energy management, and daily life applications. While in most scenarios, sensors with a fixed thermal conductivity inevitably mismatch the analogous parameter of the medium being measured, thus causing the distortion and inaccurate detection of original temperature fields. Despite recent efforts on addressing the parameter-mismatch issue, all current solutions are constrained to a fixed working medium whereas a more universal sensor should function in a variety of scenes. Here, we report a dynamic thermal sensor capable of highly accurate measurements in diverse working environments. Remarkably, thanks to the highly tunable thermal conductivity of the expanded-plane structure, this sensor works effect on background mediums with a wide range of conductivity. Such a development greatly enhances the robustness and adaptability of thermal sensors, setting a solid foundation for applications in multi-physical sensing scenarios. △ Less

Submitted 6 January, 2024; originally announced January 2024.

Convective Heat Transfer in Porous Materials

Authors: Peng Jin, Gaole Dai, Fubao Yang

Abstract: Thermal convection stands out as an exceptionally efficient thermal transport mechanism, distinctly separate from conduction and radiation. Yet, the inherently elusive nature of fluid motion poses challenges in accurately controlling convective heat flow. While recent innovations have harnessed thermal convection to achieve effective thermal conductivity, fusing thermal convection in liquids and t… ▽ More Thermal convection stands out as an exceptionally efficient thermal transport mechanism, distinctly separate from conduction and radiation. Yet, the inherently elusive nature of fluid motion poses challenges in accurately controlling convective heat flow. While recent innovations have harnessed thermal convection to achieve effective thermal conductivity, fusing thermal convection in liquids and thermal conduction in solids together to form hybrid thermal metamaterials is still challenging. In this review, we introduce the latest progress in convective heat transfer. Leveraging the right porous materials as a medium allows for a harmonious balance and synergy between convection and conduction, establishing stable heat and fluid flows. This paves the way for the innovative advancements in transformation thermotics. These findings demonstrate the remarkable tunability of convective heat transport in complex multicomponent thermal metamaterials. △ Less

Submitted 23 September, 2023; originally announced September 2023.

Controlling mass and energy diffusion with metamaterials

Authors: Fubao Yang, Zeren Zhang, Liujun Xu, Zhoufei Liu, Peng Jin, Pengfei Zhuang, Min Lei, Jinrong Liu, Jian-Hua Jiang, Xiaoping Ouyang, Fabio Marchesoni, Jiping Huang

Abstract: Diffusion driven by temperature or concentration gradients is a fundamental mechanism of energy and mass transport, which inherently differs from wave propagation in both physical foundations and application prospects. Compared with conventional schemes, metamaterials provide an unprecedented potential for governing diffusion processes, based on emerging theories like the transformation and the sc… ▽ More Diffusion driven by temperature or concentration gradients is a fundamental mechanism of energy and mass transport, which inherently differs from wave propagation in both physical foundations and application prospects. Compared with conventional schemes, metamaterials provide an unprecedented potential for governing diffusion processes, based on emerging theories like the transformation and the scattering cancellation theory, which enormously expanded the original concepts and suggest innovative metamaterial-based devices. We hereby use the term "diffusionics" to generalize these remarkable achievements in various energy (e.g., heat) and mass (e.g., particles and plasmas) diffusion systems. For clarity, we categorize the numerous studies appeared during the last decade by diffusion field (i.e., heat, particles, and plasmas) and discuss them from three different perspectives: the theoretical perspective, to detail how the transformation principle is applied to each diffusion field; the application perspective, to introduce various intriguing metamaterial-based devices, such as cloaks and radiative coolers; and the physics perspective, to connect with concepts of recent concern, such as non-Hermitian topology, nonreciprocal transport, and spatiotemporal modulation. We also discuss the possibility of controlling diffusion processes beyond metamaterials. Finally, we point out several future directions for diffusion metamaterial research, including the integration with artificial intelligence and topology concepts. △ Less

Submitted 15 February, 2024; v1 submitted 9 September, 2023; originally announced September 2023.

Comments: This review article has been published in Reviews of Modern Physics, volume 96, 015002 (2024)

Journal ref: Reviews of Modern Physics, volume 96, 015002 (2024)

Click Metamaterials: Fast Acquisition of Thermal Conductivity and Functionality Diversities

Authors: Chengmeng Wang, Peng Jin, Fubao Yang, Liujun Xu, Jiping Huang

Abstract: Material science is an important foundation of modern society development, covering significant areas like chemosynthesis and metamaterials. Click chemistry provides a simple and efficient paradigm for achieving molecular diversity by incorporating modified building blocks into compounds. In contrast, most metamaterial designs are still case by case due to lacking a fundamental mechanism for achie… ▽ More Material science is an important foundation of modern society development, covering significant areas like chemosynthesis and metamaterials. Click chemistry provides a simple and efficient paradigm for achieving molecular diversity by incorporating modified building blocks into compounds. In contrast, most metamaterial designs are still case by case due to lacking a fundamental mechanism for achieving reconfigurable thermal conductivities, largely hindering design flexibility and functional diversity. Here, we propose a universal concept of click metamaterials for fast realizing various thermal conductivities and functionalities. Tunable hollow-filled unit cells are constructed to mimic the modified building blocks in click chemistry. Different hollow-filled arrays can generate convertible thermal conductivities from isotropy to anisotropy, allowing click metamaterials to exhibit adaptive thermal functionalities. The straightforward structures enable full-parameter regulation and simplify engineering preparation, making click metamaterials a promising candidate for practical use in other diffusion and wave systems. △ Less

Submitted 6 January, 2024; v1 submitted 30 August, 2023; originally announced August 2023.

Comments: Here, click metamaterials have been proposed and swiftly generate variable thermal conductivities and functionalities by using tunable hollow-filled cells akin to the modified building blocks in click chemistry. This breakthrough holds the promise to transform applications in a range of diffusion and wave systems, thereby having a profound impact on the development of materials science

An Empirical Study of Large-Scale Data-Driven Full Waveform Inversion

Authors: Peng Jin, Yinan Feng, Shihang Feng, Hanchen Wang, Yinpeng Chen, Benjamin Consolvo, Zicheng Liu, Youzuo Lin

Abstract: This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained… ▽ More This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on OpenFWI, a collection of large-scale, multi-structural, synthetic datasets published recently. In particular, we train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K pairs of seismic data and velocity maps in total. Our experiments demonstrate that training on the combined dataset yields an average improvement of 13.03% in MAE, 7.19% in MSE and 1.87% in SSIM compared to each split dataset, and an average improvement of 28.60%, 21.55% and 8.22% in the leave-one-out generalization test. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement, where our largest model yields an average improvement of 20.06%, 13.39% and 0.72% compared to the smallest one. △ Less

Submitted 24 April, 2024; v1 submitted 28 July, 2023; originally announced July 2023.

13.56MHz Rectifying Diodes Based on Metal Halide Perovskite

Authors: Peng Jin, Xuehui Xu, Zeng Chen, Xu Chen, Tianyu Liu, Hanbo Zhu, Xinya Chen, Yang, Yang

Abstract: The increasing use of portable and wireless technologies has led to a growing focus on radio-frequency identification (RFID) tags. Among the various devices in RFID tags, rectifying diodes are the most demanding in terms of high-frequency performance, and these diodes are dominated by organic materials. However, their intrinsic low carrier mobility largely limits the rectifying ability of organic… ▽ More The increasing use of portable and wireless technologies has led to a growing focus on radio-frequency identification (RFID) tags. Among the various devices in RFID tags, rectifying diodes are the most demanding in terms of high-frequency performance, and these diodes are dominated by organic materials. However, their intrinsic low carrier mobility largely limits the rectifying ability of organic diodes. As an alternative, metal halide perovskites (MHPs) possess high carrier mobility, making them potential candidates for higher-frequency applications. Whereas their ion-migration issue may deteriorate their high-frequency performance. In this study, we report rectifying diodes based on MHPs that can rectify an incoming sinusoidal signal at 13.56 MHz. The diodes exhibit a high rectification ratio of 1.9 x 103 at 1 V and can rectify signals at even higher frequencies. We designed a triangular wave detection method to measure the intensity of ion-migration at different frequencies. Interestingly, the ion-migration did not occur at such a high frequency. The high-frequency stagnant ions and excellent carrier mobility make MHPs unexpectedly suitable for high-frequency applications, providing a promising solution to ion-migration issues and paving the way for perovskites in high-frequency areas. △ Less

Submitted 1 July, 2023; originally announced July 2023.

Comments: 19pages, 8 figures, research article, not published

$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

Authors: Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Yanhua Liu, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin

Abstract: Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that… ▽ More Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/ △ Less

Submitted 7 September, 2023; v1 submitted 21 June, 2023; originally announced June 2023.

Comments: 20 pages, 11 figures

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Authors: Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu

Abstract: Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computation… ▽ More Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science. △ Less

Submitted 8 June, 2023; originally announced June 2023.

Comments: 80 pages, 11 figures

Auto-Linear Phenomenon in Subsurface Imaging

Authors: Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Zicheng Liu, Youzuo Lin

Abstract: Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demonstrates there is only a linear mapping be… ▽ More Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demonstrates there is only a linear mapping between the latent spaces of the two domains, and the decoder requires paired data for training. This paper extends this direction by demonstrating that only linear mapping necessitates paired data, while both the encoder and decoder can be learned from their respective domains through self-supervised learning. This unveils an intriguing phenomenon (named Auto-Linear) where the self-learned features of two separate domains are automatically linearly correlated. Compared with existing methods, our Auto-Linear has four advantages: (a) solving both forward and inverse modeling simultaneously, (b) applicable to different subsurface imaging tasks and achieving markedly better results than previous methods, (c)enhanced performance, especially in scenarios with limited paired data and in the presence of noisy data, and (d) strong generalization ability of the trained encoder and decoder. △ Less

Submitted 21 May, 2024; v1 submitted 27 April, 2023; originally announced May 2023.

Experimental observation on a low-rank tensor model for eigenvalue problems

Authors: Jun Hu, Pengzhan Jin

Abstract: Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator and the harmonic oscillator. Experimental results show the superiority of the polynomial-based low-rank tensor model (PLTM) compared to the tensor neural network (TNN). We also test such low-rank architectures for the cla… ▽ More Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator and the harmonic oscillator. Experimental results show the superiority of the polynomial-based low-rank tensor model (PLTM) compared to the tensor neural network (TNN). We also test such low-rank architectures for the classification problem on the MNIST dataset. △ Less

Submitted 1 February, 2023; originally announced February 2023.

Deep learning-assisted active metamaterials with heat-enhanced thermal transport

Authors: Peng Jin, Liujun Xu, Guoqiang Xu, Jiaxin Li, Cheng-Wei Qiu, Jiping Huang

Abstract: Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working-temperature ranges, and the need for manual interventio… ▽ More Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working-temperature ranges, and the need for manual intervention, which remain long-term and tricky obstacles for the most advanced self-adaptive metamaterials. To surmount these barriers, we introduce heat-enhanced thermal diffusion metamaterials powered by deep learning. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, we design two metadevices with on-demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments. △ Less

Submitted 3 November, 2023; v1 submitted 11 January, 2023; originally announced January 2023.

Comments: This paper has been accepted for publication in Advanced Materials

Tunable liquid-solid hybrid thermal metamaterials with a topology transition

Authors: Peng Jin, Jinrong Liu, Liujun Xu, Jun Wang, Xiaoping Ouyang, Jian-Hua Jiang, Jiping Huang

Abstract: Thermal metamaterials provide rich control of heat transport which is becoming the foundations of cutting-edge applications ranging from chip cooling to biomedical. However, due to the fundamental laws of physics, the manipulation of heat is much constrained in conventional thermal metamaterials where effective heat conduction with Onsager reciprocity dominates. Here, through the inclusion of ther… ▽ More Thermal metamaterials provide rich control of heat transport which is becoming the foundations of cutting-edge applications ranging from chip cooling to biomedical. However, due to the fundamental laws of physics, the manipulation of heat is much constrained in conventional thermal metamaterials where effective heat conduction with Onsager reciprocity dominates. Here, through the inclusion of thermal convection and breaking the Onsager reciprocity, we unveil a regime in thermal metamaterials and transformation thermotics that goes beyond effective heat conduction. By designing a liquid-solid hybrid thermal metamaterial, we demonstrate a continuous switch from thermal cloaking to thermal concentration in one device with external tuning. Underlying such a switch is a topology transition in the virtual space of the thermotic transformation which is achieved by tuning the liquid flow via external control. These discoveries illustrate the extraordinary heat transport in complex multi-component thermal metamaterials and pave the way toward an unprecedented regime of heat manipulation. △ Less

Submitted 4 February, 2023; v1 submitted 29 August, 2022; originally announced August 2022.

Journal ref: PNAS 120, e2217068120 (2023)

Blackhole-Inspired Thermal Trapping with Graded Heat-Conduction Metadevices

Authors: Liujun Xu, Jinrong Liu, Peng Jin, Guoqiang Xu, Jiaxin Li, Xiaoping Ouyang, Ying Li, Cheng-Wei Qiu, Jiping Huang

Abstract: Black holes are one of the most intriguing predictions of general relativity. So far, metadevices have enabled analogous black holes to trap light or sound in laboratory spacetime. However, trapping heat in a conductive ambient is still challenging because diffusive behaviors are directionless. Inspired by black holes, we construct graded heat-conduction metadevices to achieve thermal trapping, re… ▽ More Black holes are one of the most intriguing predictions of general relativity. So far, metadevices have enabled analogous black holes to trap light or sound in laboratory spacetime. However, trapping heat in a conductive ambient is still challenging because diffusive behaviors are directionless. Inspired by black holes, we construct graded heat-conduction metadevices to achieve thermal trapping, resorting to the imitated advection produced by graded thermal conductivities rather than the trivial solution of using insulation materials to confine thermal diffusion. We experimentally demonstrate thermal trapping for guiding hot spots to diffuse towards the center. Graded heat-conduction metadevices have advantages in energy-efficient thermal regulation because the imitated advection has a similar temperature field effect to the realistic advection that is usually driven by external energy sources. These results also provide insights into correlating transformation thermotics with other disciplines such as cosmology for emerging heat control schemes. △ Less

Submitted 19 August, 2022; v1 submitted 18 May, 2022; originally announced May 2022.

Journal ref: National Science Review, 10, nwac159 (2023)

An Intriguing Property of Geophysics Inversion

Authors: Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin

Abstract: Inversion techniques are widely used to reconstruct subsurface physical properties (e.g., velocity, conductivity) from surface-based geophysical measurements (e.g., seismic, electric/magnetic (EM) data). The problems are governed by partial differential equations (PDEs) like the wave or Maxwell's equations. Solving geophysical inversion problems is challenging due to the ill-posedness and high com… ▽ More Inversion techniques are widely used to reconstruct subsurface physical properties (e.g., velocity, conductivity) from surface-based geophysical measurements (e.g., seismic, electric/magnetic (EM) data). The problems are governed by partial differential equations (PDEs) like the wave or Maxwell's equations. Solving geophysical inversion problems is challenging due to the ill-posedness and high computational cost. To alleviate those issues, recent studies leverage deep neural networks to learn the inversion mappings from measurements to the property directly. In this paper, we show that such a mapping can be well modeled by a very shallow (but not wide) network with only five layers. This is achieved based on our new finding of an intriguing property: a near-linear relationship between the input and output, after applying integral transform in high dimensional space. In particular, when dealing with the inversion from seismic data to subsurface velocity governed by a wave equation, the integral results of velocity with Gaussian kernels are linearly correlated to the integral of seismic data with sine kernels. Furthermore, this property can be easily turned into a light-weight encoder-decoder network for inversion. The encoder contains the integration of seismic data and the linear transformation without need for fine-tuning. The decoder only consists of a single transformer block to reverse the integral of velocity. Experiments show that this interesting property holds for two geophysics inversion problems over four different datasets. Compared to much deeper InversionNet, our method achieves comparable accuracy, but consumes significantly fewer parameters. △ Less

Submitted 16 June, 2022; v1 submitted 28 April, 2022; originally announced April 2022.

MIONet: Learning multiple-input operators via tensor product

Authors: Pengzhan Jin, Shuai Meng, Lu Lu

Abstract: As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several neural operators have been recently developed. However, all the existing neural operators are only designed to learn operators defined on a single Banach space, i.e., the input of the operator is a single function. Here… ▽ More As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several neural operators have been recently developed. However, all the existing neural operators are only designed to learn operators defined on a single Banach space, i.e., the input of the operator is a single function. Here, for the first time, we study the operator regression via neural networks for multiple-input operators defined on the product of Banach spaces. We first prove a universal approximation theorem of continuous multiple-input operators. We also provide detailed theoretical analysis including the approximation error, which provides a guidance of the design of the network architecture. Based on our theory and a low-rank approximation, we propose a novel neural operator, MIONet, to learn multiple-input operators. MIONet consists of several branch nets for encoding the input functions and a trunk net for encoding the domain of the output function. We demonstrate that MIONet can learn solution operators involving systems governed by ordinary and partial differential equations. In our computational examples, we also show that we can endow MIONet with prior knowledge of the underlying system, such as linearity and periodicity, to further improve the accuracy. △ Less

Submitted 12 February, 2022; originally announced February 2022.

Extremely Weak Supervision Inversion of Multi-physical Properties

Authors: Shihang Feng, Peng Jin, Xitong Zhang, Yinpeng Chen, David Alumbaugh, Michael Commer, Youzuo Lin

Abstract: Multi-physical inversion plays a critical role in geophysics. It has been widely used to infer various physical properties~(such as velocity and conductivity). Among those inversion problems, some are explicitly governed by partial differential equations~(PDEs), while others are not. Without explicit governing equations, conventional multi-physical inversion techniques will not be feasible and dat… ▽ More Multi-physical inversion plays a critical role in geophysics. It has been widely used to infer various physical properties~(such as velocity and conductivity). Among those inversion problems, some are explicitly governed by partial differential equations~(PDEs), while others are not. Without explicit governing equations, conventional multi-physical inversion techniques will not be feasible and data-driven inversion requires expensive full labels. To overcome this issue, we develop a new data-driven multi-physics inversion technique with extremely weak supervision. Our key finding is that the pseudo labels can be constructed by learning the local relationship among geophysical properties at very sparse well-logging locations. We explore a multi-physics inversion problem from two distinct measurements~(seismic and EM data) to three geophysical properties~(velocity, conductivity, and CO$_2$ saturation). Our results show that we are able to invert for properties without explicit governing equations. Moreover, the label data on three geophysical properties can be significantly reduced by 50 times~(from 100 down to only 2 locations). △ Less

Submitted 22 May, 2022; v1 submitted 3 February, 2022; originally announced February 2022.

Comments: Accepted for presentation at The International Meeting for Applied Geoscience & Energy (IMAGE)

Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop

Authors: Peng Jin, Xitong Zhang, Yinpeng Chen, Sharon Xiaolei Huang, Zicheng Liu, Youzuo Lin

Abstract: This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. This problem is mathematically formulated by a second order partial differential equation (PDE), but is hard to solve. Moreover, acquiring velocity map is extremely expensive, making it impractical to scale up a supervised… ▽ More This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. This problem is mathematically formulated by a second order partial differential equation (PDE), but is hard to solve. Moreover, acquiring velocity map is extremely expensive, making it impractical to scale up a supervised approach to train the mapping from seismic data to velocity maps with convolutional neural networks (CNN). We address these difficulties by integrating PDE and CNN in a loop, thus shifting the paradigm to unsupervised learning that only requires seismic data. In particular, we use finite difference to approximate the forward modeling of PDE as a differentiable operator (from velocity map to seismic data) and model its inversion by CNN (from seismic data to velocity map). Hence, we transform the supervised inversion task into an unsupervised seismic data reconstruction task. We also introduce a new large-scale dataset OpenFWI, to establish a more challenging benchmark for the community. Experiment results show that our model (using seismic data alone) yields comparable accuracy to the supervised counterpart (using both seismic data and velocity map). Furthermore, it outperforms the supervised model when involving more seismic data. △ Less

Submitted 18 March, 2022; v1 submitted 14 October, 2021; originally announced October 2021.

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

Authors: Pengzhan Jin, Zhen Zhang, Aiqing Zhu, Yifa Tang, George Em Karniadakis

Abstract: We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate… ▽ More We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions. We then perform several experiments including the pendulum, double pendulum and three-body problems to investigate the expressivity and the generalization ability of SympNets. The simulation results show that even very small size SympNets can generalize well, and are able to handle both separable and non-separable Hamiltonian systems with data points resulting from short or long time steps. In all the test cases, SympNets outperform the baseline models, and are much faster in training and prediction. We also develop an extended version of SympNets to learn the dynamics from irregularly sampled data. This extended version of SympNets can be thought of as a universal model representing the solution to an arbitrary Hamiltonian system. △ Less

Submitted 19 August, 2020; v1 submitted 11 January, 2020; originally announced January 2020.

Two-dimensional modeling of the self-limiting oxidation in silicon and tungsten nanowires

Authors: Mingchao Liu, Peng Jin, Zhiping Xu, Dorian A. H. Hanaor, Yixiang Gan, Changqing Chen

Abstract: Self-limiting oxidation of nanowires has been previously described as a reaction- or diffusion-controlled process. In this letter, the concept of finite reactive region is introduced into a diffusion-controlled model, based upon which a two-dimensional cylindrical kinetics model is developed for the oxidation of silicon nanowires and is extended for tungsten. In the model, diffusivity is affected… ▽ More Self-limiting oxidation of nanowires has been previously described as a reaction- or diffusion-controlled process. In this letter, the concept of finite reactive region is introduced into a diffusion-controlled model, based upon which a two-dimensional cylindrical kinetics model is developed for the oxidation of silicon nanowires and is extended for tungsten. In the model, diffusivity is affected by the expansive oxidation reaction induced stress. The dependency of the oxidation upon curvature and temperature is modeled. Good agreement between the model predictions and available experimental data is obtained. The developed model serves to quantify the oxidation in two-dimensional nanostructures and is expected to facilitate their fabrication via thermal oxidation techniques. https://doi.org/10.1016/j.taml.2016.08.002 △ Less

Submitted 20 November, 2019; originally announced November 2019.

Comments: Creative Commons License

Journal ref: Theoretical and Applied Mechanics Letters, Volume 6, Issue 5, September 2016, Pages 195-199

Sputtered Spontaneously Nano-porous VO2-based Films via PTFE Self-Template: Localized Surface Plasmon Resonance Induced Robust Optical Performance for Solar Glazing Application

Authors: Shiwei Long, Xun Cao, Rong Huang, Fang Xu, Ning Li, Aibin Huang, Guangyao Sun, Shanhu Bao, Hongjie Luo, Ping Jin

Abstract: The PTFE (Teflon) has been selected as the self-template structural material in preparation of VO2 films using reactive magnetron sputtering systems and post annealing progress. Spontaneous random nano-porous structures of VO2 films growing on quartz glasses have been deliberately established via bottom-up processing through this novel and facile approach. The nano-porous VO2 films exhibit an exce… ▽ More The PTFE (Teflon) has been selected as the self-template structural material in preparation of VO2 films using reactive magnetron sputtering systems and post annealing progress. Spontaneous random nano-porous structures of VO2 films growing on quartz glasses have been deliberately established via bottom-up processing through this novel and facile approach. The nano-porous VO2 films exhibit an excellent optical performance based on localized surface plasmon resonance (LSPR), with ultrahigh luminous transmittance (Tlum) up to 78.0% and the promoted solar modulation ability (ΔTsol) of 14.1%. Meanwhile, the ingenious microstructure of film provides an antireflection function from multiple perspectives in visible light, with the potential of the windshield on vehicles for smart solar modulation. The nano-porous films expand the practical application of thermochromic VO2 to a fire-new field, breaking the optical performance envelope of single-layer dense VO2 film away, and offering a universal method to prepare homogeneous nano-porous structures for thin films. △ Less

Submitted 8 February, 2019; originally announced February 2019.

Towards Commercializing Vanadium Dioxide Films: Investigation of the Impact of Different Interface on the Deterioration Process for Largely Extended Service Life

Authors: Tianci Chang, Xun Cao, Ning Li, Shiwei Long, Ying Zhu, Jian Huang, Hongjie Luo, Ping Jin

Abstract: Long term stability is the most pressing issue that impedes commercialization of Vanadium Dioxide (VO2) based functional films, which show a gradual loss of relative phase transition performance, especially in humid conditions when serving as smart windows. Here, we investigated the impact of different interface on the deterioration process of VO2 films and proposed a novel encapsulation structure… ▽ More Long term stability is the most pressing issue that impedes commercialization of Vanadium Dioxide (VO2) based functional films, which show a gradual loss of relative phase transition performance, especially in humid conditions when serving as smart windows. Here, we investigated the impact of different interface on the deterioration process of VO2 films and proposed a novel encapsulation structure for largely extended service life. Hydrophobic and stable hafnium dioxide (HfO2) layers have been incorporated with VO2 films for encapsulated surfaces and cross sections. With modified thickness and structure of HfO2 layers, the degradation process of VO2 can be effectively suppressed. The proposed films can retain stable phase transition performances under high relative humidity (90%) and temperature (60 Celsius) over 100 days, which is equal to about 16 years in the real environment. Improving the stability of VO2 materials is a necessary step towards commercializing production of high performance films for long term use. △ Less

Submitted 5 February, 2019; originally announced February 2019.

Comments: 24 pages, 15 figures

Journal ref: Matter 1, 734-744, 2019

Observation of unusual optical band structure of CH3NH3PbI3 perovskite single crystal

Authors: Wei Huang, Shizhong Yue, Yu Liu, Laipan Zhu, Peng Jin, Qing Wu, Yang Zhang, Yanan Chen, Kong Liu, Ping Liang, Shengchun Qu, Zhijie Wang, Yonghai Chen

Abstract: Extensive efforts have been undertaken on the photoelectric physics of hybrid organolead halide perovskites to unveil the reason for the attractive photovoltaic performance. Yet, the resulting evidences are far from being fully conclusive. Herein, we provide another direct support for this issue. In addition to the observation on the conventional band edge at 1.58 eV that presents a blueshift towa… ▽ More Extensive efforts have been undertaken on the photoelectric physics of hybrid organolead halide perovskites to unveil the reason for the attractive photovoltaic performance. Yet, the resulting evidences are far from being fully conclusive. Herein, we provide another direct support for this issue. In addition to the observation on the conventional band edge at 1.58 eV that presents a blueshift toward temperature increase, interestingly, we also observe an unusual optical band edge at 1.48 eV in CH3NH3PbI3 perovskite single crystals. Contrary to the conventional band edge, this one shows an obvious redshift toward the enhancement in temperature, in agreement with the Varshni relation. More interestingly, the unusual band edge exhibits a series of obvious absorption and photocurrent signals, but the according photoluminescence signals are not observable. This indicates that this band edge is particularly beneficial for the photovoltaic effect due to the inhibited radiative recombination. The kinetics on photo-involved charge transition and transfer are investigated using the pump-probe photoconductivity technique, and a changeable band structure model was proposed. △ Less

Submitted 4 March, 2018; v1 submitted 13 July, 2017; originally announced July 2017.

Comments: 32 pages, 16 figures, and 1 table

Luminosity measurements for the R scan experiment at BESIII

Authors: M. Ablikim, M. N. Achasov, S. Ahmed, X. C. Ai, O. Albayrak, M. Albrecht, D. J. Ambrose, A. Amoroso, F. F. An, Q. An, J. Z. Bai, O. Bakina, R. Baldini Ferroli, Y. Ban, D. W. Bennett, J. V. Bennett, N. Berger, M. Bertani, D. Bettoni, J. M. Bian, F. Bianchi, E. Boger, I. Boyko, R. A. Briere, H. Cai , et al. (405 additional authors not shown)

Abstract: By analyzing the large-angle Bhabha scattering events $e^{+}e^{-}$ $\to$ ($γ$)$e^{+}e^{-}$ and diphoton events $e^{+}e^{-}$ $\to$ $γγ$ for the data sets collected at center-of-mass (c.m.) energies between 2.2324 and 4.5900 GeV (131 energy points in total) with the upgraded Beijing Spectrometer (BESIII) at the Beijing Electron-Positron Collider (BEPCII), the integrated luminosities have been measur… ▽ More By analyzing the large-angle Bhabha scattering events $e^{+}e^{-}$ $\to$ ($γ$)$e^{+}e^{-}$ and diphoton events $e^{+}e^{-}$ $\to$ $γγ$ for the data sets collected at center-of-mass (c.m.) energies between 2.2324 and 4.5900 GeV (131 energy points in total) with the upgraded Beijing Spectrometer (BESIII) at the Beijing Electron-Positron Collider (BEPCII), the integrated luminosities have been measured at the different c.m. energies, individually. The results are the important inputs for R value and $J/ψ$ resonance parameter measurements. △ Less

Submitted 11 February, 2017; originally announced February 2017.

Measurements of Baryon Pair Decays of $χ_{cJ}$ Mesons

Authors: M. Ablikim, M. N. Achasov, O. Albayrak, D. J. Ambrose, F. F. An, Q. An, J. Z. Bai, Y. Ban, J. Becker, J. V. Bennett, M. Bertani, J. M. Bian, E. Boger, O. Bondarenko, I. Boyko, R. A. Briere, V. Bytev, X. Cai, O. Cakir, A. Calcaterra, G. F. Cao, S. A. Cetin, J. F. Chang, G. Chelkov, G. Chen , et al. (326 additional authors not shown)

Abstract: Using 106 $\times 10^{6}$ $ψ^{\prime}$ decays collected with the BESIII detector at the BEPCII, three decays of $χ_{cJ}$ ($J=0,1,2$) with baryon pairs ($\llb$, $\ssb$, $\SSB$) in the final state have been studied. The branching fractions are measured to be $\cal{B}$$(χ_{c0,1,2}\rightarrowΛ\barΛ) =(33.3 \pm 2.0 \pm 2.6)\times 10^{-5}$, $(12.2 \pm 1.1 \pm 1.1)\times 10^{-5}$,… ▽ More Using 106 $\times 10^{6}$ $ψ^{\prime}$ decays collected with the BESIII detector at the BEPCII, three decays of $χ_{cJ}$ ($J=0,1,2$) with baryon pairs ($\llb$, $\ssb$, $\SSB$) in the final state have been studied. The branching fractions are measured to be $\cal{B}$$(χ_{c0,1,2}\rightarrowΛ\barΛ) =(33.3 \pm 2.0 \pm 2.6)\times 10^{-5}$, $(12.2 \pm 1.1 \pm 1.1)\times 10^{-5}$, $(20.8 \pm 1.6 \pm 2.3)\times 10^{-5}$; $\cal{B}$$(χ_{c0,1,2}\rightarrowΣ^{0}\barΣ^{0})$ = $(47.8 \pm 3.4 \pm 3.9)\times 10^{-5}$, $(3.8 \pm 1.0 \pm 0.5)\times 10^{-5}$, $(4.0 \pm 1.1 \pm 0.5) \times 10^{-5}$; and $\cal{B}$$(χ_{c0,1,2}\rightarrowΣ^{+}\barΣ^{-})$ = $(45.4 \pm 4.2 \pm 3.0)\times 10^{-5}$, $(5.4 \pm 1.5 \pm 0.5)\times 10^{-5}$, $(4.9 \pm 1.9 \pm 0.7)\times 10^{-5}$, where the first error is statistical and the second is systematic. Upper limits on the branching fractions for the decays of $χ_{c1,2}\rightarrowΣ^{0}\barΣ^{0}$, $Σ^{+}\barΣ^{-}$, are estimated to be $\cal{B}$$(χ_{c1}\rightarrowΣ^{0}\barΣ^{0}) < 6.2\times 10^{-5}$, $\cal{B}$$(χ_{c2}\rightarrowΣ^{0}\barΣ^{0}) < 6.5\times 10^{-5}$, $\cal{B}$$(χ_{c1}\rightarrowΣ^{+}\barΣ^{-}) < 8.7\times 10^{-5}$ and $\cal{B}$$(χ_{c2}\rightarrowΣ^{+}\barΣ^{-}) < 8.8\times 10^{-5}$ at the 90% confidence level. △ Less

Submitted 4 March, 2013; v1 submitted 9 November, 2012; originally announced November 2012.

Comments: 13 pages, 7 figures, 3 tables

Journal ref: Phys. Rev. D 87, 032007 (2013)

Study of magnetic alloy cores for HIRFL-CSRm compressor cavity

Authors: Li-Rong Mei, Zhe Xu, You-Jin Yuan, Peng Jin, Zhi-Bin Bian, Hong-Wei Zhao

Abstract: For selecting the properly magnetic alloy (MA) material to load the RF compression cavity, the measurement of the MA cores which is produced by Liyuan Company has been carried out at IMP. We measured 4 kinds of MA core materials, type V1, V2, A1 and A2. And we mainly focus on the permeability, quality factor (Q value) and shunt impedance of the MA core. The MA cores which have higher permeability,… ▽ More For selecting the properly magnetic alloy (MA) material to load the RF compression cavity, the measurement of the MA cores which is produced by Liyuan Company has been carried out at IMP. We measured 4 kinds of MA core materials, type V1, V2, A1 and A2. And we mainly focus on the permeability, quality factor (Q value) and shunt impedance of the MA core. The MA cores which have higher permeability, lower Q value and higher shunt impedance will be selected to load RF compression cavity. According to the results of measurement, the type V1, V2 and A2 material will be chosen as candidate to load RF cavity. △ Less

Submitted 4 December, 2010; originally announced December 2010.

Comments: 8pages This article has contributed to Chinese Physics C

Trigger efficiencies at BES III

Authors: N. Berger, K. Zhu, Z. A. Liu, D. P. Jin, H. Xu, W. X. Gong, K. Wang, G. F. Cao

Abstract: Trigger efficiencies at BES III were determined for both the J/psi and psi' data taking of 2009. Both dedicated runs and physics datasets are used; efficiencies are presented for Bhabha-scattering events, generic hadronic decay events involving charged tracks, dimuon events and psi' -> pi+pi-J/psi, J/psi -> l+l- events (l an electron or muon). The efficiencies are found to lie well above 99% for a… ▽ More Trigger efficiencies at BES III were determined for both the J/psi and psi' data taking of 2009. Both dedicated runs and physics datasets are used; efficiencies are presented for Bhabha-scattering events, generic hadronic decay events involving charged tracks, dimuon events and psi' -> pi+pi-J/psi, J/psi -> l+l- events (l an electron or muon). The efficiencies are found to lie well above 99% for all relevant physics cases, thus fulfilling the BES III design specifications. △ Less

Submitted 11 November, 2010; originally announced November 2010.

Comments: 6 pages, 4 figures

Journal ref: Chin.Phys.C34:1779-1784,2010