Related papers: Computational Prediction of Muon Stopping Sites Us…
Finding the stopping site of the muon in a muon-spin relaxation experiment is one of the main problems of muon spectroscopy, and computational techniques that make use of quantum chemistry simulations can be of great help when looking for…
Significant progress has recently been made in calculating muon stopping sites using density functional theory. The technique aims to address two of the most common criticisms of the muon-spin spectroscopy ($\mu^+$SR) technique, namely,…
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy…
The information accessible from a muon-spin relaxation experiment is often limited since we lack knowledge of the precise muon stopping site. We demonstrate here the possibility of localizing a spin polarized muon in a known stopping state…
One of the most fundamental limitations of a muon-spin relaxation experiment can be the lack of knowledge of the implantation site of the muon and the uncertainty about the muon's perturbation of its host. Here we review some of the work…
We present the results of a muon-spin relaxation (muSR) investigation into magnetic ordering in several families of layered quasi-two-dimensional molecular antiferromagnets based on transition metal ions such as S=1/2 Cu2+ bridged with…
It is essential to know the arrangement of the atoms in a material in order to compute and understand its properties. Searching for stable structures of materials using first-principles electronic structure methods, such as density…
Crystal structure prediction has traditionally relied on prototype-based seeding, approaches that often bias sampling toward known low-energy basins and overlook metastable polymorphs with unconventional symmetries. Here, we introduce…
In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network approach to predict the…
Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust…
Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy…
We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential…
In this letter we propose a new methodology for crystal structure prediction, which is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an…
Transverse-field muon-spin rotation ($\mu$SR) experiments were performed on a single crystal sample of the non-centrosymmetric system MnSi. The observed angular dependence of the muon precession frequencies matches perfectly the one of the…
The magnetic ground state of single crystalline RuO$_2$ was investigated by the muon spin rotation/relaxation ($\mu$SR) experiment. The spin precession signal due to the spontaneous internal magnetic field $B_{\rm loc}$, which is expected…
We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method allows one to predict the most stable…
Compositional disorder is common in crystal compounds. In these compounds, some atoms are randomly distributed at some crystallographic sites. For such compounds, randomness forms many non-identical independent structures. Thus, calculating…
At the Paul Scherrer Institut (PSI), we are currently working on the development of a high-precision apparatus with the aim of searching for the muon electric dipole moment (EDM) with unprecedented sensitivity. The underpinning principle of…
Crystal structure prediction is a central problem of theoretical crystallography and materials science, which until mid-2000s was considered intractable. Several methods, based on either energy landscape exploration$^{1,2}$ or, more…
Muon spin relaxation/rotation (muSR) is a vital technique for probing the superconducting gap structure, pairing symmetry and time reversal symmetry breaking, enabling an understanding of the mechanisms behind the unconventional…