相关论文: Machine Learning of Interstellar Chemical Inventor…
Chemical abundance determinations from stellar spectra are challenged by observational noise, limitations in stellar models, and departures from simplifying assumptions. While traditional and supervised machine learning methods have made…
The past decade has led to significant improvements in our understanding of the physical structure of the molecular cores of cold dark clouds. Observational efforts, in combination with improved knowledge of cloud structure, now provide…
The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is thus an important parameter for many astrochemical studies. This parameter is usually determined with…
Ice mantles play a crucial role in shaping the astrochemical inventory of molecules during star and planet formation. Small-scale molecular processes have a profound impact on large-scale astronomical evolution. The areas of solid-state…
Interstellar ices are layers of molecules deposited on !ne dust grains in dark and dense molecular cloud cores. Subsurface ice has been considered in a few astrochemical models, which have shown that it can be of great importance. The aim…
We have studied the chemical enrichment history of the interstellar medium through an analysis of the n-dimensional stellar abundances space. This work is a non-parametric analysis of the stellar chemical abundance space. The main goal is…
Ongoing surveys are in the process of measuring the chemical abundances in large numbers of stars, with the ultimate goal of reconstructing the formation history of the Milky Way using abundances as tracers. However, interpretation of these…
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…
Astrochemistry lies at the nexus of astronomy, chemistry, and molecular physics. On the basis of precise laboratory data, a rich collection of more than 200 familiar and exotic molecules have been identified in the interstellar medium, the…
The study of the metal enrichment of the intra-cluster and inter-galactic media (ICM and IGM) represents a direct means to reconstruct the past history of star formation, the role of feedback processes and the gas-dynamical processes which…
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual sampling of this space, for example…
Transient microstructure in the diffuse interstellar medium has been observed towards galactic and extragalactic sources for decades, usually in lines of atoms and ions, and, more recently, in molecular lines. Evidently, there is a…
Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
We review recent studies dealing with the generation of machine learning models of molecular and solid properties. The models are trained and validated using standard quantum chemistry results obtained for organic molecules and materials…
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,…
Understanding the physical conditions of dark molecular clouds and star forming regions is an inverse problem subject to complicated chemistry that varies non-linearly with time and the physical environment. In this paper we apply a…
Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect -- perfect candidates selectively attach to and influence only targets, leaving off-targets…
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical…
Machine learning techniques are now routinely encountered in research laboratories across the globe. Impressive progress has been made through ML and AI techniques with regards to large data set processing. This progress has increased the…