Related papers: Machine Learning Refinements to Metallicity-Depend…
Elemental and isotopic abundances are the fossils of galactic archaeology. The observed [X/Fe]-[Fe/H] relations in the Galactic bulge and disk and the mass-metallicity relation of galaxies are roughly reproduced with chemodynamical…
(Abridged) We reassess the alpha-element abundance ratios (Ne/O, S/O, Ar/O) with respect to metallicity in ~1000 spectra of Galactic and extragalactic HII regions and star-forming galaxies (SFGs) of the local Universe. Using the DEep…
Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictions for laboratory and commercial superalloys, with $R^2>0.8$…
We present a spatially-resolved HII region study of the gas-phase metallicity, ionization parameter, and ISM pressure maps of 6 local star-forming and face-on spiral galaxies from the TYPHOON program. Self-consistent metallicity, ionization…
The decomposition of the Solar system abundances of heavy isotopes into their s- and r- components plays a key role in our understanding of the corresponding nuclear processes and the physics and evolution of their astrophysical sites. We…
Low-temperature plasmas are partially ionized gases, where ions and neutrals coexist in a highly reactive environment. This creates a rich chemistry, which is often difficult to understand in its full complexity. In this work, we develop a…
Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a…
Machine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. In interstellar sources further along the path of star formation, such as those where a protostar itself has been…
We describe an SPH model for chemical enrichment and radiative cooling in cosmological simulations of structure formation. This model includes: i) the delayed gas restitution from stars by means of a probabilistic approach designed to…
Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder…
In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine…
The study of chemical abundances in stars with planets is an important ingredient for the models of formation and evolution of planetary systems. In order to determine accurate abundances, it is crucial to have a reliable set of atmospheric…
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from…
Fitting a data set with a parametrized model can be seen geometrically as finding the global minimum of the chi^2 hypersurface, depending on a set of parameters {P_i}. This is usually done using the Levenberg-Marquardt algorithm. The main…
Assuming that in the atmospheres of low-mass, metal-poor red giant stars, 1-D models based on LTE accurately predict the abundance of iron from Fe II, we derive a globular cluster metallicity scale based on the equivalent widths of Fe II…
Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
Machine learning potential enables molecular dynamics simulations of systems beyond the capability of classical force fields. The traditional approach to develop structural sets for training machine learning potential typically generate a…
It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools…
A sparse modeling approach is proposed for analyzing scanning tunneling microscopy topography data, which contains numerous peaks corresponding to surface atoms. The method, based on the relevance vector machine with $\mathrm{L}_1$…