Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMachine Learning on Neutron and X-Ray Scattering
107
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
The advent of massive data repositories has propelled machine learning techniques to the front lines of many scientific fields, and exploring new frontiers by leveraging the predictive power of machine learning will greatly accelerate big data-assisted discovery. In this work, we show that graph-based neural networks can be used to predict the near edge x-ray absorption structure spectra of molecules with exceptional accuracy. The predicted spectra reproduce nearly all the prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Our study demonstrates that machine learning models can achieve practically the same accuracy as first-principles calculations in predicting complex physical quantities, such as spectral functions, but at a fraction of the cost.
Resonant x-ray scattering is performed near the Mn K-absorption edge for an epitaxial thin film of BiMnO3. The azimuthal angle dependence of the resonant (003) peak (in monoclinic indices) is measured with different photon polarizations; for the $sigmatopi$ channel a 3-fold symmetric oscillation is observed in the intensity variation, while the $sigmatosigma$ scattering intensity remains constant. These features are accounted for in terms of the peculiar ordering of the manganese 3d orbitals in BiMnO3. It is demonstrated that the resonant peak persists up to 770 K with an anomaly around 440 K; these high and low temperatures coincide with the structural transition temperatures, seen in bulk, with and without a symmetry change, respectively. A possible relationship of the orbital order with the ferroelectricity of the system is discussed.
Polarized neutron reflectometry (PNR) is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge to parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from PNR data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator (TI)-ferromagnetic insulator heterostructure Bi$_2$Se$_3$/EuS, exhibiting proximity magnetism, in good agreement with the results of conventional fitting. We further analyze a more challenging PNR profile of the TI-antiferromagnet heterostructure (Bi,Sb)$_2$Te$_3$/Cr$_2$O$_3$, and identify possible interfacial proximity magnetism in this material. We anticipate the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.
Resonance anomalous surface x-ray scattering (RASXS) technique was applied to electrochemical interface studies. It was used to determine the chemical states of electrochemically formed anodic oxide monolayers on platinum surface. It is shown that RASXS exhibits strong polarization dependence when the surface is significantly modified. The polarization dependence is demonstrated for three examples; anodic oxide formation, sulfate adsorption, and CO adsorption on platinum surfaces. s- and p- polarization RASXS data were simulated with the latest version of ab initio multiple scattering calculations (FEFF8.2). Elementary theoretical considerations are also presented for the origin of the polarization dependence in RASXS.
X-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes important information about the local chemical environment of an absorbing atom, including coordination number, symmetry, and oxidation state. Interpreting XANES spectra is a key step towards understanding the structural and electronic properties of materials, and as such, extracting structural and electronic descriptors from XANES spectra is akin to solving a challenging inverse problem. Existing methods rely on empirical fingerprints, which are often qualitative or semiquantitative and not transferable. In this paper, we present a machine learning-based approach, which is capable of classifying the local coordination environments of the absorbing atom from simulated K-edge XANES spectra. The machine learning classifiers can learn important spectral features in a broad energy range without human bias and once trained, can make predictions on the fly. The robustness and fidelity of the machine learning method are demonstrated by an average 86% accuracy across the wide chemical space of oxides in eight 3d transition-metal families. We found that spectral features beyond the preedge region play an important role in the local structure classification problem especially for the late 3d transition-metal elements.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
