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Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text, and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum…
High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in…
As building blocks of dust, rocky planets, and even complex life, the chemical elements heavier than hydrogen (H) and helium (He) - called "metals" in astronomy - play an essential role in our Universe and its evolution. Up to Fe and Ni,…
Although first considered as too diluted for the formation of molecules in-situ and too harsh an environment for their survival, the interstellar medium has turned out to host a rich palette of molecular species: to date, 256 species have…
The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the…
The determination of chemical abundances in star-forming galaxies and the study of their evolution on cosmological timescales are powerful tools for understanding galaxy formation and evolution. This contribution presents the latest results…
Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days…
High quality archival spectra of interstellar absorption from C I toward 9 stars, taken with the Goddard High Resolution Spectrograph on the Hubble Space Telescope, were analyzed. Our sample was supplemented by two sight lines, 23 Ori and…
The field of astrochemistry has seen major advances triggered by the completion of new powerful radio telescopes, with gains in sensitivity of receivers and in bandwidth. To date, about 330 molecular species are detected, in interstellar…
The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to…
The task here is to predict the toxicological activity of chemical compounds based on the Tox21 dataset, a benchmark in computational toxicology. After a domain-specific overview of chemical toxicity, we discuss current computational…
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread…
One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense…
Modern observations of the interstellar medium (ISM) in galaxies detect a variety of atomic and molecular species. The goal is to connect these observations to the astrochemical properties of the ISM. 3D hydro-chemical simulations attempt…
Recent progress of deep generative models in the vision and language domain has stimulated significant interest in more structured data generation such as molecules. However, beyond generating new random molecules, efficient exploration and…
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications,…
Constraining parameters such as the initial mass function high-mass slope and the frequency of type Ia supernovae is of critical importance in the ongoing quest to understand galactic physics and create realistic hydrodynamical simulations.…
Synthesized in the cores of stars and supernovae, most metals disperse over cosmic scales and are ultimately deposited well outside the gravitational potential of their host galaxies. Since their presence is well visible through their X-ray…
Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological…
The presence of carbon-chain molecules in the interstellar medium (ISM) has been known since the early 1970s and $>130$ such species have been identified to date, making up $\sim 43$% of the total of detected ISM molecules. They are…