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Due to the high penetrating power of cosmic ray muons, they can be used to probe very thick and dense objects. As charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With…

Image and Video Processing · Electrical Eng. & Systems 2024-10-11 Jesus J. Valencia , Adam A. Hecht , C. L. Morris , E. Guardincerri , D. Poulson , J. Bacon , J. M. Durham

Nd2Hf2O7, belonging to the family of geometrically frustrated cubic rare earth pyrochlore oxides, was recently identified to order antiferromagnetically below T_N = 0.55 K with an all-in/all-out arrangement of Nd3+ moments, however with a…

Strongly Correlated Electrons · Physics 2017-12-15 V. K. Anand , D. L. Abernathy , D. T. Adroja , A. D. Hillier , P. K. Biswas , B. Lake

The dynamical mean field theory (DMFT) has become a standard technique for the study of strongly correlated models and materials overcoming some of the limitations of density functional approaches based on local approximations. An important…

Strongly Correlated Electrons · Physics 2015-10-28 K. Hallberg , D. J. García , Pablo S. Cornaglia , Jorge I. Facio , Y. Núñez-Fernández

The automation of ab initio simulations is essential in view of performing high-throughput (HT) computational screenings oriented to the discovery of novel materials with desired physical properties. In this work, we propose algorithms and…

The metal-organic framework (MOF) MFU-4l containing Co(II) centers and Cl- ligands has recently shown promising redox activity. Aiming for further improved MOF catalysts for oxidation processes employing molecular oxygen we present a…

Materials Science · Physics 2013-11-27 Jelena Jelic , Dmytro Denysenko , Dirk Volkmer , Karsten Reuter

We propose and implement a novel hybrid meta-heuristic optimization algorithm for the identification of non-collinear global ground-states in magnetic systems. The inputs to this optimization scheme are directly from non-collinear density…

Materials Science · Physics 2023-10-03 Guy C. Moore , Matthew K. Horton , Kristin A. Persson

Muon Spin Rotation/Relaxation/Resonance ($\mu$SR) is a versatile and powerful non-destructive technology for investigating the magnetic properties of materials at the microscopic level. The $\mu$SR technique typically utilizes fully spin…

Large scale Density Functional Theory (DFT) based electronic structure calculations are highly time consuming and scale poorly with system size. While semi-empirical approximations to DFT result in a reduction in computational time versus…

Materials Science · Physics 2016-12-21 Ganesh Hegde , R. Chris Bowen

Noncollinear (NC) magnetism and spin-orbit coupling (SOC) are indispensable for predictive ab initio materials simulations with pronounced relativistic effects and magnetic frustration, yet they significantly increase the cost of…

We present a detailed study of the magnetic properties of weakly ferromagnetic/quantum critical Nb$_{1-y}$Fe$_{2+y}$ using muon spin rotation and relaxation ($\mu$SR). By means of an angular dependent study of the muon spin rotation signal…

Strongly Correlated Electrons · Physics 2022-10-19 J. Willwater , D. Eppers , T. Kimmel , E. Sadrollahi , F. J. Litterst , F. M. Grosche , C. Baines , S. Süllow

Carbon capture is vital for decarbonizing heavy industries such as steel and chemicals. Metal-organic frameworks (MOFs), with their high surface area and structural tunability, are promising materials for CO2 capture. This study focuses on…

The developments of quantum computing algorithms and experiments for atomic scale simulations have largely focused on quantum chemistry for molecules, while their application in condensed matter systems is scarcely explored. Here we present…

The online reconstruction of muon tracks in High Energy Physics experiments is a highly demanding task, typically performed with programmable logic boards, such as FPGAs. Complex analytical algorithms are executed in a quasi-real-time…

High Energy Physics - Experiment · Physics 2022-12-21 Matteo Migliorini , Jacopo Pazzini , Andrea Triossi , Marco Zanetti , Alberto Zucchetta

Machine Learned Interatomic Potentials (MLIPs) offer a powerful combination of abilities for accelerating theoretical spectroscopy calculations utilising both ensemble sampling and trajectory post-processing for inclusion of vibronic…

Chemical Physics · Physics 2025-10-23 Jacob Eller , Nicholas D. M. Hine

Muon spin spectroscopy is one of the most powerful tools to investigate the microscopic properties of superconductors. In this manuscript, an overview on some of the main achievements obtained by this technique in the iron-based…

Superconductivity · Physics 2015-06-16 P. Carretta , R. De Renzi , G. Prando , S. Sanna

Spin-phonon coupling is the main drive of spin relaxation and decoherence in solid-state semiconductors at finite temperature. Controlling this interaction is a central problem for many disciplines, ranging from magnetic resonance to…

Materials Science · Physics 2022-08-23 Alessandro Lunghi

Locality of compact one-electron orbitals expanded strictly in terms of local subsets of basis functions can be exploited in density functional theory (DFT) to achieve linear growth of computation time with systems size, crucial in…

Computational Physics · Physics 2021-10-01 Yifei Shi , Jessica Karaguesian , Rustam Z. Khaliullin

Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in…

Materials Science · Physics 2023-10-25 Stefan Riemelmoser , Carla Verdi , Merzuk Kaltak , Georg Kresse

Density functional theory (DFT) is widely used to predict chemical properties, but its accuracy is limited by functional approximations and their approximate self-consistent densities. Density-corrected DFT (DC-DFT) is the study of the…

Chemical Physics · Physics 2023-07-21 Hayoung Yu , Suhwan Song , Seungsoo Nam , Kieron Burke , Eunji Sim

Density Functional Theory (DFT) allows for predicting all the chemical and physical properties of molecular systems from first principles by finding an approximate solution to the many-body Schr\"odinger equation. However, the cost of these…

Machine Learning · Computer Science 2025-06-03 Majdi Hassan , Cristian Gabellini , Hatem Helal , Dominique Beaini , Kirill Neklyudov