Related papers: Classifying metal-poor stars with machine learning…
Nucleosynthesis of heavy nuclei in metal-poor stars is generally thought to occur via the r-process because the r-process is a primary process that would have operated early in the Galaxy's history. This idea is strongly supported by the…
The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we…
Machine Learning (ML) is accelerating the progress of materials prediction and classification, with particular success in CGNN designs. While classical ML methods remain accessible, advanced deep networks are still challenging to build and…
In unveiling the nature of the first stars, the main astronomical clue is the elemental compositions of the second generation of stars, observed as extremely metal-poor (EMP) stars, in our Milky Way Galaxy. However, no observational…
Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be used to identify hidden trends in the data. However, these…
Machine learning (ML) has revolutionized medical prognostics by integrating advanced algorithms with clinical data to enhance disease prediction, risk assessment, and patient outcome forecasting. This comprehensive review critically…
We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as…
Mass-to-light versus colour relations (MLCRs), derived from stellar population synthesis models, are widely used to estimate galaxy stellar masses (M$_*$) yet a detailed investigation of their inherent biases and limitations is still…
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining the spatial distribution of small polarons is essential to understand…
We propose a unified model for the nucleosynthesis of heavy (A > 57) elements in stars. The neutron flux can be set to describe neutron capture in arbitrary neutron flux. Our approach solves the coupled differential equations, that describe…
Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address…
In cancer therapeutics, protein-metal binding mechanisms critically govern the pharmacokinetics and targeting efficacy of drugs, thereby fundamentally shaping the rational design of anticancer metallodrugs. While conventional laboratory…
To better characterize the abundance patterns produced by the r-process, we have derived new abundances or upper limits for the heavy elements zinc (Zn), yttrium (Y), lanthanum (La), europium (Eu), and lead (Pb). Our sample of 161…
Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements ("hits") to identify those that form…
We use cosmological, magnetohydrodynamical simulations of Milky Way-mass galaxies from the Auriga project to study their enrichment with rapid neutron capture (r-process) elements. We implement a variety of enrichment models from both…
A long-standing scientific puzzle has been to explain the origin of the heaviest elements in the Universe and, more particularly, the production of the elements heavier than iron up to uranium. The rapid neutron capture process (or…
The post-merger phase of binary neutron star (BNS) mergers encodes valuable information about the equation of state (EOS) of supranuclear matter. Extracting this information from the analysis of the post-merger waveforms remains challenging…
Neural networks can be used to identify phases and phase transitions in condensed matter systems via supervised machine learning. Readily programmable through modern software libraries, we show that a standard feed-forward neural network…
Due to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with…