Related papers: Machine Learning Refinements to Metallicity-Depend…

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. This can be an extremely tedious process, often relying significantly on trial and error. Machine…

The evolution and distribution of metals within galaxies are critical for understanding galactic evolution and star formation processes, but the mechanisms responsible for shaping this distribution remain uncertain. In this study, we carry…

In computer chip manufacturing, the study of etch patterns on silicon wafers, or metrology, occurs on the nano-scale and is therefore subject to large variation from small, yet significant, perturbations in the manufacturing environment. An…

Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity…

By molecular dynamics simulations and free energy calculations based on Monte Carlo method, the detailed balance between Pt cluster isomers was investigated. For clusters of n<=50, stationary equilibrium is achieved in 100 ns in the…

We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into…

We applied the decision trees (random forest) machine-learning technique for the large experimental materials dataset PAULING FILE, compiled from the world's peer-reviewed literature. The training and validation data were extracted from the…

Elemental abundances of stars are the result of the complex enrichment history of their galaxy. Interpretation of observed abundances requires flexible modeling tools to explore and quantify the information about Galactic chemical evolution…

With the advent of large spectroscopic surveys the amount of high quality chemo-dynamical data in the Milky Way (MW) increased tremendously. Accurately and correctly capturing and explaining the detailed features in the high-quality…

Stellar evolutionary tracks for $0.12 \le M/M_\odot \le 1.0$ have been computed for each of several variations in the abundances of C, N, and O, assuming mass-fraction helium abundances $Y = 0.25$ and $0.29$, and 11 metallicities in the…

This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to both develop a better understanding of the…

(Abridged) We present an implementation of stellar evolution and chemical feedback for smoothed particle hydrodynamics (SPH) simulations. We consider the timed release of individual elements by both massive (Type II supernovae and stellar…

Different statistical multifragmentation models have been used to study isoscaling, i.e. the factorization of the isotope ratios from two reactions, into fugacity terms of proton and neutron number, R21(N,Z)=Y2(N,Z)/Y1(N,Z)=C*exp(a*N+b*Z).…

We present a study of accurate stellar parameters and iron abundances for 39 giants and 16 dwarfs in the 13 open clusters IC2714, IC4651, IC4756, NGC2360, NGC2423, NGC2447 (M93), NGC2539, NGC2682 (M67), NGC3114, NGC3680, NGC4349, NGC5822,…

Aims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Leveraging CatBoost and deep learning architectures, we utilize…

As powerful as machine learning (ML) techniques are in solving problems involving data with large dimensionality, explaining the results from the fitted parameters remains a challenging task of utmost importance, especially in physics…

Proposing new materials by atom substitution based on periodic table similarity is a conventional strategy of searching for materials with desired property. We introduce a machine learning frame work that promotes this paradigm to be…

The relationships between stellar mass, gas-phase metallicity and star formation rate (i.e. the Mass-Metallicity, MZR, and the Fundamental Metallcity Relation, FMR) in the local Universe are revisited by fully anchoring the metallicity…

The chemical abundances of metal-poor stars are excellent sources of information for setting new constraints on models of Galactic chemical evolution at low metallicities. In this paper we present an attempt to fit the elemental abundances…

The metallicity is a critical parameter that affects the correct determination fundamental characteristics stellar cluster and has important implications in Galactic and Stellar evolution research. Fewer than 10 % of the 2174 currently…