相关论文: Predicting Beta Decay Energy with Machine Learning
A recent proposed method for $\alpha$-decay energies ($Q_\alpha$) [J.M. Dong, W. Zuo, and W. Scheid, Phys. Rev. Lett. \textbf{107}, 012501 (2011)] can reproduce experimental data of superheavy nuclei (SHN) with an $rms$-value of less than…
Ab-initio calculations of nuclear masses, the binding energy and the $\alpha$ decay half-lives are intractable for heavy nucleus, because of the curse of dimensionality in many body quantum simulations as proton number($\mathrm{N}$) and…
$\alpha$ decay is an important probe for studying the structure of heavy and superheavy nuclei, in which the $\alpha$-particle preformation ($P_{\alpha}$) is a key physical quantity for describing decay half-lives. This work develops a…
The nuclear matrix elements $M^{0\nu}$ of the neutrinoless double beta decay ($0\nu\beta\beta$) of most nuclei with known $2\nu\beta\beta$-decay rates are systematically evaluated using the Quasiparticle Random Phase Approximation (QRPA)…
Nuclear double-beta decays are a unique probe to search for new physics beyond the Standard Model. Still-unknown particles, non-standard interactions, or the violation of fundamental symmetries would affect the decay kinematic, creating…
Most machine learning techniques are based upon statistical learning theory, often simplified for the sake of computing speed. This paper is focused on the uncertainty aspect of mathematical modeling in machine learning. Regression analysis…
A central question in machine learning is how reliable the predictions of a trained model are. Reliability includes the identification of instances for which a model is likely not to be trusted based on an analysis of the learning system…
The precision of double-beta ($\beta\beta$) decay experimental half-lives and their uncertainties is reevaluated. A complementary analysis of the decay uncertainties indicates deficiencies due to small size of statistical samples, and…
Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property prediction, but their evaluation as machine learning targets has been complicated by the absence…
The nuclear matrix elements $M^{0\nu}$ of the neutrinoless double beta decay ($0\nu\beta\beta$) of most nuclei with known $2\nu\beta\beta$-decay rates are systematically evaluated using the Quasiparticle Random Phase Approximation (QRPA)…
Binding energy is a fundamental thermodynamic property that governs molecular interactions, playing a crucial role in fields such as healthcare and the natural sciences. It is particularly relevant in drug development, vaccine design, and…
An earlier publication "The implication of the atomic effects in neutrinoless double beta (0$\nu\beta\beta$) decay" written by Mei and Wei has motivated us to compare the decay Q value ($Q_{\beta\beta}$) derived from the decay of the parent…
Comprehensive description of the phenomenology of the $\beta\beta$ decay is given, with emphasis on the nuclear physics aspects. After a brief review of the neutrino oscillation results and of motivation to test the lepton number…
The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control,…
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…
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets.…
Advances in statistical learning theory present the opportunity to develop statistical models of quantum many-body systems exhibiting remarkable predictive power. The potential of such ``theory-thin'' approaches is illustrated with the…
Density functional theory and its optimization algorithm are the main methods to calculate the properties in the field of materials. Although the calculation results are accurate, it costs a lot of time and money. In order to alleviate this…
Leveraging the unique properties of quantum mechanics, Quantum Machine Learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach their boundaries. However, similarly to classical machine…
Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention the relevant dynamical control. Here we introduce proof of…