相关论文: Machine Learning Insights into Discrepancies Betwe…

The fission barrier energy is a fundamental property of nuclear structure that governs the stability of nuclei against fission, directly affecting their spontaneous fission half-lives and the formation of superheavy elements. However,…

Background: The masses of about 2500 nuclei have been measured experimentally, however more than 7000 isotopes are predicted to exist in the nuclear landscape from H (Z=1) to Og (Z=118) based on various theoretical calculations. Exploring…

Machine learning has proven to be a valuable tool to approximate functions in high-dimensional spaces. Unfortunately, analysis of these models to extract the relevant physics is never as easy as applying machine learning to a large dataset…

Different machine learning (ML) models are proposed in the present work to predict DFT-quality barrier heights (BHs) from semiempirical quantum-mechanical (SQM) calculations. The ML models include multi-task deep neural network, gradient…

This paper compares the performances of three supervised machine learning algorithms in terms of predictive ability and model interpretation on structured or tabular data. The algorithms considered were scikit-learn implementations of…

The concept of fission barrier - a parameter which enters in quantitative estimates of various observables related to nuclear fission - is presented from the point of view of theory based on the picture of nuclear deformation and energy…

Theoretical uncertainties in the predictions of inner fission barrier heights in superheavy elements have been investigated in a systematic way for a set of state-of-the-art covariant energy density functionals which represent major classes…

Using the ETFSI (extended Thomas-Fermi plus Strutinsky integral) method, we have calculated the fission barriers of nearly 2000 exotic nuclei, including all the neutron-rich nuclei up to A=318 that are expected to be relevant to the…

Predictions of nuclear properties far from measured data are inherently imprecise because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model…

We implement machine learning algorithms to nuclear data. These algorithms are purely data driven and generate models that are capable to capture intricate trends. Gradient boosted trees algorithm is employed to generate a trained model…

Very low-yield nuclear tests pose a major verification challenge for the zero-yield standard of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The zero-yield standard prohibits any explosive experiment that produces a self-sustaining…

Based on Extreme Gradient Boosting (XGBoost) framework optimized via Bayesian hyperparameter tuning, we investigated the {\alpha}-decay energy and half-life of superheavy nuclei. By incorporating key nuclear structural features-including…

We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new…

Combining physics with machine learning models has advanced the performance of machine learning models in many different applications. In this paper, we evaluate adding a weak physics constraint, i.e., a physics-based empirical…

Nowadays, manufacturing sectors harness the power of machine learning and data science algorithms to make predictions for the optimization of mechanical and microstructure properties of fabricated mechanical components. The application of…

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material…

The prevailing data-driven machine learning has been plagued by the absence of physics knowledge and the scarcity of data. We implement the physics-model informed prior into Bayesian machine learning to evaluate the energy dependence of…

Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at…

Machine learning algorithms are growing increasingly popular in particle physics analyses, where they are used for their ability to solve difficult classification and regression problems. While the tools are very powerful, they may often be…

Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to recent efforts to obtain these relationships with machine…