Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMachine-Learning X-ray Absorption Spectra to Quantitative Accuracy
99
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
With the examples of the C $K$-edge in graphite and the B $K$-edge in hexagonal BN, we demonstrate the impact of vibrational coupling and lattice distortions on the X-ray absorption near-edge structure (XANES) in 2D layered materials. Theoretical XANES spectra are obtained by solving the Bethe-Salpeter equation of many-body perturbation theory, including excitonic effects through the correlated motion of core-hole and excited electron. We show that accounting for zero-point motion is important for the interpretation and understanding of the measured X-ray absorption fine structure in both materials, in particular for describing the $sigma^*$-peak structure.
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.
Inelastic losses are crucial to a quantitative analysis of x-ray absorption spectra. However, current treatments are semi-phenomenological in nature. Here a first-principles, many-pole generalization of the plasmon-pole model is developed for improved calculations of inelastic losses. The method is based on the GW approximation for the self-energy and real space multiple scattering calculations of the dielectric function for a given system. The model retains the efficiency of the plasmon-pole model and is applicable both to periodic and aperiodic materials over a wide energy range. The same many-pole model is applied to extended GW calculations of the quasiparticle spectral function. This yields estimates of multi-electron excitation effects, e.g., the many-body amplitude factor $S_0^2$ due to intrinsic losses. Illustrative calculations are compared with other GW calculations of the self-energy, the inelastic mean free path, and experimental x-ray absorption spectra.
We report the development of XASdb, a large database of computed reference X-ray absorption spectra (XAS), and a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 300,000 K-edge X-ray absorption near-edge spectra (XANES) for over 30,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. We will demonstrate that the ELSIE algorithm, which combines 33 weak learners comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of Veidt, an open source machine learning library for materials science.
The formation and disassociation of excitons plays a crucial role in any photovoltaic or photocatalytic application. However, excitonic effects are seldom considered in materials discovery studies due to the monumental computational cost associated with the examination of these properties. Here, we study the excitonic properties of nearly 50 photocatalysts using state-of-the-art Bethe-Salpeter formalism. These $sim$ 50 materials were recently recognized as promising photocatalysts for CO$_2$ reduction through a data-driven screening of 68,860 materials. Here, we propose three screening criteria based on the optical properties of these materials, taking excitonic effects into account, to further down select 6 materials. Remarkably we find a strong correlation between the exciton binding energies obtained from the Bethe-Salpeter formalism and those obtained from the computationally much less-expensive Wannier-Mott model for these chemically diverse $sim$ 50 materials. This work presents a new paradigm towards the inclusion of excitonic effects in future materials discovery for solar-energy harvesting applications.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
