Land-use change simulation and assessment of driving factors in the loess hilly region - a case study as Pengyang County

The main objective of this study is to evaluate the land-use change and its relationship with its driving factors in the loess hilly region. In this study, a case study was carried out in Pengyang County. We set two land-use demand scenarios (a baseline scenario (scenario 1) and a real land-use requirement scenario (scenario 2)) during year 2001-2005 via assuming the effect of driving factors on land-use change keeps stable from 1993 to 2005. Two simulated land-use patterns of 2005 are therefore achieved accordingly by use of the conversion of land use and its effects model at small regional extent. Kappa analyses are conducted to compare each simulated land-use pattern with the reality. Results show that (1) the associated kappa values were decreased from 0.83 in 1993-2000 to 0.27 (in scenario 1) and 0.23 (in scenario 2) in 2001-2005 and (2) forest and grassland were the land-use types with highest commission errors, which implies that conversion of both the land-use types mentioned above is the main determinant of change of kappa values. Our study indicates the land-use change was driven by the synthetic multiply factors including natural and social-economic factors (e.g., slope, aspect, elevation, distance to road, soil types, and population dense) in 1993-2000 until "Grain for Green Project" was implemented and has become the dominant factor in 2001-2005

Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics

Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFS and spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries

Direct prediction of phonon density of states with Euclidean neural networks

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here we demonstrate the direct prediction of phonon density of states using only atomic species and positions as input. We apply Euclidean neural networks, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of $\sim 10^{3}$ examples with over 64 atom types. Our predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements,and is naturally suited to efficiently predict alloy systems without additional computational cost. We demonstrate the potential of our network by predicting a broad number of high phononic specific heat capacity materials. Our work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors.Comment: 21 pages total, 5 main figures + 16 supplementary figures. To appear in Advanced Science (2021

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Capturing dynamical correlations using implicit neural representations

The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor, S(Q, $\omega$), with inelastic neutron or x-ray scattering techniques and comparing this against a calculated dynamical model. Here, we develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. We benchmark this approach on a Linear Spin Wave Theory (LSWT) simulator and advanced inelastic neutron scattering data from the square-lattice spin-1 antiferromagnet La$_2$NiO$_4$. We find that the model predicts the unknown parameters with excellent agreement relative to analytical fitting. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data, without the need for human-guided peak finding and fitting algorithms. This prototypical approach promises a new technology for this field to automatically detect and refine more advanced models for ordered quantum systems.Comment: 12 pages, 7 figure

Fluctuation-driven, topology-stabilized order in a correlated nodal semimetal

The interplay between strong electron correlation and band topology is at the forefront of condensed matter research. As a direct consequence of correlation, magnetism enriches topological phases and also has promising functional applications. However, the influence of topology on magnetism remains unclear, and the main research effort has been limited to ground state magnetic orders. Here we report a novel order above the magnetic transition temperature in magnetic Weyl semimetal (WSM) CeAlGe. Such order shows a number of anomalies in electrical and thermal transport, and neutron scattering measurements. We attribute this order to the coupling of Weyl fermions and magnetic fluctuations originating from a three-dimensional Seiberg-Witten monopole, which qualitatively agrees well with the observations. Our work reveals a prominent role topology may play in tailoring electron correlation beyond ground state ordering, and offers a new avenue to investigate emergent electronic properties in magnetic topological materials.Comment: 32 pages, 15 figure

From a thin membrane to an unbounded solid : dynamics and instabilities in radial motion of nonlinearly viscoelastic spheres

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 85-88).In this thesis, a theoretical investigation of the dynamic motion of spherically symmetric bodies is presented, considering nonlinear viscoelastic material responses. To explore the stability thresholds of the dynamic motion and to compare them with available formula for the quasi-static limit, the present formulation employs a generalized constitutive relation and accounts for different loading scenarios. Specifically for instantaneously applied load, by studying the entire spectrum of radii ratios of the spherical body, ranging from a thin membrane to an unbounded medium, we show that geometric effects can significantly reduce the dynamic stability limit while viscoelasticity has a stabilizing effect. Additionally, we show that in finite spheres, rate-dependence can induce a bifurcation of the long-time response. The stability thresholds derived in this thesis, together with their geometric and constitutive sensitivities, can inform the design of more resilient material systems that employ soft materials in dynamic settings, with examples including seismic bearings that are designed to absorb shocks but often fail due to rupture of internal cavities, and thin inflatable membrane structures like rubber balloons, which may exhibit snap-through instabilities and consequent ruptures. By accounting for rate-dependence, the results of this thesis also shed light on the response of biological materials to dynamic load and the possible instabilities that can lead to injury in vulnerable organs, such as the brain and the lungs. Moreover, while modern therapeutic ultrasound techniques intentionally generate cavities within the tissue, the present investigation of the material response to harmonic excitations across various frequencies can lead to safer practice.by Zhantao Chen.S.M

Volume-controlled cavity expansion for probing of local elastic properties in soft materials

Cavity expansion can be used to measure the local nonlinear elastic properties in soft materials, regardless of the specific damage or instability mechanism that it may ultimately induce. To that end, we introduce a volume-controlled cavity expansion procedure and an accompanying method that builds on the Cavitation Rheology technique [J. A. Zimberlin et al., Soft Matter, 2007, 3, 763-767], but without relying on the maximum recorded pressure. This is achieved by determining an effective radius of the cavity that is based on the volume measurements, and is further supported by numerical simulations. Applying this method to PDMS samples, we show that it consistently collapses the experimental curves to the theoretical prediction of cavity expansion prior to the occurrence of fracture or cavitation, thus resulting in high precision measurement with less than 5% of scatter and good agreement with results obtained via conventional techniques. Moreover, since it does not require visual tracking of the cavity, this technique can be applied to measure the nonlinear elastic response in opaque samples

Study on the Effect of Mineral Particle Sizes on the Spectral Characteristics of Sound and Vibrations in Rock Drilling

This study aimed to investigate the relationships between particle sizes and spectral characteristics of the sound or vibration signals generated in rock drilling processes. Several drilling experiments were conducted on concrete specimens of different aggregate sizes. By using an indoor signal acquisition and analysis system, data from the sound waves and vibrations were collected, the characteristic signals were extracted, and the spectral characteristics of the sound and vibrations of different aggregate sizes were identified. An approach based on frequency band analysis was adopted. In this approach, the average amplitude of each frequency band was calculated after frequency segmentation. The overall distribution trend in the frequency domain is conveniently observed, and the trend line patterns reflect the effect of aggregate sizes. The time-domain features of sound and vibration, such as the amplitude of sound pressure and vibration, are important indexes that roughly reflect grain sizes. The general trend is that the larger the amplitudes of sound and vibration, the larger the grain size. The frequency domain features of sound and vibration, such as the distribution of energy at high and low frequencies, can also reflect grain sizes. The general trend is that the larger the high-frequency composition of sound and vibration, the smaller the aggregate size. These results are useful for revealing the influence mechanism of rock particle sizes on the vibration spectral characteristics in drilling processes. The study provides a possibility for developing a method to evaluate the information on rock structures collected from drilling vibration or sound signals for the fine exploration of geological surveys