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The properties of mobile impurities in quantum magnets are fundamental for our understanding of strongly correlated materials and may play a key role in the physics of high-temperature superconductivity. Hereby, the motion of hole-like…

Strongly Correlated Electrons · Physics 2021-10-25 K. K. Nielsen , M. A. Bastarrachea-Magnani , T. Pohl , G. M. Bruun

Based on high-throughput density functional theory calculations, we evaluate the local magnetic moments and M\"ossbauer properties for Fe-based intermetallic compounds and employ machine learning to map the local crystalline environments to…

Materials Science · Physics 2025-03-21 Bo Zhao , Hongbin Zhang

We discuss the determination of the parton substructure of hadrons by casting it as a peculiar form of pattern recognition problem in which the pattern is a probability distribution, and we present the way this problem has been tackled and…

High Energy Physics - Phenomenology · Physics 2020-08-31 Stefano Forte , Stefano Carrazza

The comparative analysis of different sets of the parton distribution functions (PDFs), based on the description of the whole sets of experimental data of electromagnetic form factors of the proton and neutron, is made in the framework of…

High Energy Physics - Phenomenology · Physics 2015-06-19 O. V. Selyugin

We extract two nonsinglet nucleon Parton Distribution Functions from lattice QCD data for reduced Ioffe-time pseudodistributions. We perform such analysis within the NNPDF framework, considering data coming from different lattice ensembles…

High Energy Physics - Phenomenology · Physics 2021-03-17 Luigi Del Debbio , Tommaso Giani , Joseph Karpie , Kostas Orginos , Anatoly Radyushkin , Savvas Zafeiropoulos

The determination of the parton distribution functions (PDFs) is crucial for a complete understanding of the protons and neutrons that make most of the visible matter in the universe. Years of dedicated studies have yielded a quite precise…

High Energy Physics - Phenomenology · Physics 2018-10-02 Pia Zurita

In our present publication we will continue studying the effects of the magnetostatic energy on the magnetization behavior of FMSMAs recently started in some our publications. Our method is based on the direct minimization of our new…

Materials Science · Physics 2021-11-15 A. A. Likhachev , Yu. N. Koval

Magnetic 2D materials have achieved significantly consideration owing to their encouraging applications. A variation of these 2D materials by occurrence of defects, by the transition-metal doping or adsorption or by the surface…

Materials Science · Physics 2025-06-27 Zarah Khan , Saleem Ayaz Khan , Ayesha Zaheer , Syed Rizwan

Local positional disorder in soft, anharmonic materials has emerged as a central factor in shaping their electronic, vibrational, optical, and transport properties. Viewed mainly as a source of performance degradation, recent theoretical…

Materials Science · Physics 2025-10-24 Marios Zacharias , Jacky Even

We present a first determination of the nuclear parton distribution functions (nPDF) based on the NNPDF methodology: nNNPDF1.0. This analysis is based on neutral-current deep-inelastic structure function data and is performed up to NNLO in…

High Energy Physics - Phenomenology · Physics 2019-07-23 Rabah Abdul Khalek , Jacob J. Ethier , Juan Rojo

We demonstrate spatial mapping of the local and nano-scale structure of thin film objects using spatially resolved PDF analysis of synchrotron x-ray diffraction data. This is demonstrated in a lab-on-chip combinatorial array of sample spots…

Unintegrated parton density functions (uPDFs) of Quantum Chromodynamics (QCD), also known as TMDPDFs, are generally used to study details of hadronic final states in high energy lepton-hadron and hadron-hadron collisions; while the…

High Energy Physics - Phenomenology · Physics 2011-11-16 Akbari Jahan , D. K. Choudhury

In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PES) with close to first-principles accuracy. Most current MLPs rely on atomic…

Materials Science · Physics 2022-04-06 Marius Herbold , Jörg Behler

We introduce the position-dependent probability distribution function (PDF) of the smoothed matter field as a cosmological observable. In comparison to the PDF itself, the spatial variation of the position-dependent PDF is simpler to model…

Cosmology and Nongalactic Astrophysics · Physics 2021-04-29 Drew Jamieson , Marilena Loverde

We review the current state of the art in the determination of the parton substructure of the nucleon, as expressed in terms of parton distribution functions (PDFs), and probed in high-energy lepton-hadron and hadron-hadron collisions, and…

High Energy Physics - Phenomenology · Physics 2013-10-23 Stefano Forte , Graeme Watt

We present new sets of nuclear parton distribution functions (nPDFs) at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO). Our analyses are based on deeply inelastic scattering data with charged-lepton and neutrino beams…

High Energy Physics - Phenomenology · Physics 2019-12-17 Marina Walt , Ilkka Helenius , Werner Vogelsang

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…

Materials Science · Physics 2018-10-04 Johannes J. Möller , Wolfgang Körner , Georg Krugel , Daniel F. Urban , Christian Elsässer

Some of the synthesis methods and physical properties of iron-oxide based magnetic nanoparticles such as Fe3-xO4 and CoxFe3-xO4 are reviewed because of their interest in health, environmental applications, and ultra-high-density magnetic…

We have used high-resolution Extended X-ray Absorption Fine-Structure and diffraction techniques to measure the local structure of strained La$_{0.5}$Sr$_{0.5}$CoO$_3$ films under compression and tension. The lattice mismatch strain in…

Strongly Correlated Electrons · Physics 2009-11-13 C. K. Xie , J. I. Budnick , W. A. Hines , B. O. Wells , J. C. Woicik

We estimate the spatial distribution of heterogeneous physical parameters involved in the formation of magnetic domain patterns of polycrystalline thin films by using convolutional neural networks. We propose a method to obtain a spatial…

Materials Science · Physics 2023-11-03 Naoya Mamada , Masaichiro Mizumaki , Ichiro Akai , Toru Aonishi