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The electric dipole moment (EDM) plays a crucial role in determining the interaction strength of an atom with electric fields, making it paramount to quantum technologies based on coherent atomic control. We propose a scheme for engineering…
The most sensitive measurements of the electron electric dipole moment d_e have previously been made using heavy atoms. Heavy polar molecules offer a greater sensitivity to d_e because the interaction energy to be measured is typically 10^3…
Electric dipole moments are extremely sensitive probes of physics beyond the Standard Model. A vibrant experimental program is in place, with the goal of improving existing bounds on the electron and neutron electric dipole moments by one…
We present a Fortran program to compute the distribution of dipole moments of free particles for use in analyzing molecular beams experiments that measure moments by deflection in an inhomogeneous field. The theory is the same for magnetic…
Electric dipole moment (EDM) experiments are at the fore-front of search for physics beyond the standard model. The next generation searches promise to improve by several orders of magnitude the current EDM sensitivity levels.
The expression for polarized electric dipole moment of well-deformed reflection asymmetric nuclei is obtained in the framework of liquid-drop model in the case of geometrically similar proton and neutron surfaces. The expression for…
Searches for intrinsic electric dipole moments (EDMs) of nucleons, atoms and molecules are precision flavor-diagonal probes of new $CP$-odd physics, as motivated by the need to explain the matter-antimatter asymmetry in the universe. We…
We present an approach to compute the electric and magnetic dipole moments of an electron by using polarization and magnetization parts of the Dirac current. We show that these dipole moment expressions obtained by our approach in this…
We present results of the first experiment to probe for the electric dipole moment (EDM) of the electron using an $\Omega$-doublet state in a polar molecule. If the molecule is both massive and has a large molecular-fixed frame dipole…
Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological…
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory…
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for…
A new highly sensitive method of looking for electric dipole moments of charged particles in storage rings is described. The major systematic errors inherent in the method are addressed and ways to minimize them are suggested. It seems…
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach…
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and transformations. Here we show that a machine learning model,…
We study a hybrid quantum system composed of an ion and an electric dipole. We show how a trapped ion can be used to measure the small electric field generated by a classical dipole. We discuss the application of this scheme to measure the…
In many models of CP violation, the electric dipole moments (EDMs) of leptons scale as the cube of the lepton mass. In these models, the EDM of a 100 GeV heavy lepton would be a billion times greater than that of the muon, and could be as…
The article provides an overview of the behavior and detection of electric and magnetic dipole moments in free atomic and molecular nanoclusters.
We study a simple model consisting of an atomic ion and a polar molecule trapped in a single setup, taking into consideration their electrostatic interaction. We determine analytically their collective modes of excitation as a function of…
Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for…