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Recently developed instrumentation at the University of Washington has allowed for nonresonant x-ray emission spectra (XES) to be measured in a laboratory-setting with an inexpensive, easily operated system. We present a critical evaluation…

Materials Science · Physics 2016-06-22 Devon R. Mortensen , Gerald T. Seidler , Alexander S. Ditter , Pieter Glatzel

Electron energy loss spectroscopy (EELS) has been established as a powerful analytical technique for investigating the oxidation state, band structure, and dielectric properties of materials with exceptional spatial resolution. Inspired by…

Element- and site-specific resonant inelastic x-ray scattering spectroscopy (RIXS) is employed to investigate electron correlation effects in {$\rm NaV_2O_5$}. In contrast to single photon techniques, RIXS at the vanadium $L_3$ edge is able…

Strongly Correlated Electrons · Physics 2009-11-07 G. P. Zhang , T. A. Callcott , G. T. Woods , L. Lin , Brian Sales , D. Mandrus , J. He

Femtosecond transient soft X-ray Absorption Spectroscopy (XAS) is a very promising technique that can be employed at X-ray Free Electron Lasers (FELs) to investigate out-of-equilibrium dynamics for material and energy research. Here we…

{\it Ab initio} calculations of relativistic electron energy loss spectra (REELS) are carried out using a generalization of the real-space Green's function code FEFF8 which is applicable to general aperiodic materials. Our approach…

Materials Science · Physics 2007-09-04 K. Jorissen , J. J. Rehr

Out-of-distribution (OOD) prediction is often approached by restricting models to causal or invariant covariates, avoiding non-causal spurious associations that may be unstable across environments. Despite its theoretical appeal, this…

Methodology · Statistics 2026-01-06 Shuozhi Zuo , Yixin Wang

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new…

Machine Learning · Computer Science 2014-09-24 Bo Han , Bo He , Rui Nian , Mengmeng Ma , Shujing Zhang , Minghui Li , Amaury Lendasse

Reflection electron energy loss spectroscopy (EELS) in specular and off specular geometry has been employed to study the early stage of the copper phthalocyanine (CuPc) growth on Al (100) substrate. EEL spectroscopy has been a useful tool…

Materials Science · Physics 2009-11-10 A. Ruocco , M. P. Donzello , F. Evangelista , G. Stefani

There has been a veritable explosion of methods and software to perform machine-learned regression on datasets of electronic energies and forces to develop high-dimensional machine learned potential energy surfaces (ML-PESs). A major, but…

The Electron Energy-Loss Spectroscopy (EELS) and X-ray Absorption Spectroscopy (XAS) database has been completely rewritten, with an improved design, user interface and a number of new tools. The database is accessible at https://eelsdb.eu/…

Materials Science · Physics 2016-03-18 Philip Ewels , Thierry Sikora , Virginie Serin , Chris P. Ewels , Luc Lajaunie

Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various…

We develop an empirical likelihood (EL) framework for random forests and related ensemble methods, providing a likelihood-based approach to quantify their statistical uncertainty. Exploiting the incomplete $U$-statistic structure inherent…

Machine Learning · Statistics 2025-11-19 Harold D. Chiang , Yukitoshi Matsushita , Taisuke Otsu

We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a…

Computer Vision and Pattern Recognition · Computer Science 2020-07-29 Jan Funke , Fabian David Tschopp , William Grisaitis , Arlo Sheridan , Chandan Singh , Stephan Saalfeld , Srinivas C. Turaga

We analyze how accurately supervised machine learning techniques can predict the lowest energy levels of one-dimensional noninteracting ultracold atoms subject to the correlated disorder due to an optical speckle field. Deep neural networks…

Quantum Gases · Physics 2019-04-10 S. Pilati , P. Pieri

Advances in hyperspectral imaging modes including electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) bring forth the challenges of exploratory and subsequently physics-based analysis of…

Computational Physics · Physics 2020-05-22 Sergei V. Kalinin , Andrew R. Lupini , Rama K. Vasudevan , Maxim Ziatdinov

Resonant inelastic x-ray scattering (RIXS) is a well-established tool for studying electronic, nuclear and collective dynamics of excited atoms, molecules and solids. An extension of this powerful method to a time-resolved probe technique…

Chemical Physics · Physics 2015-11-03 Victor Kimberg , Nina Rohringer

Understanding the oxidation and reduction mechanisms of catalytically active transition metal nanoparticles is important to improve their application in a variety of chemical processes. In nanocatalysis the nanoparticles can undergo…

Chemical Physics · Physics 2017-05-18 Alec Lagrow , Michael Ward , David Lloyd , Pratibha Gai , Edward Boyes

X-ray absorption spectroscopy (XAS) is a powerful experimental tool to probe the local structure in materials with the core hole excitations. Here, the oxygen K-edge XAS spectra of the NaCl solution and pure water are computed by using a…

Materials Science · Physics 2023-11-03 Fujie Tang , Kefeng Shi , Xifan Wu

The mechanical properties of complex concentrated alloys (CCAs) depend on their forming phases and corresponding structures, the prediction of the phase formation for a given CCA is essential to its discovery and applications. 541 sample…

Applied Physics · Physics 2025-11-07 Jie Xiong , San-Qiang Shi , Tong-Yi Zhang

An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo…

Materials Science · Physics 2024-12-23 Tigany Zarrouk , Rina Ibragimova , Albert P. Bartók , Miguel A. Caro