Related papers: A fast neural emulator for interstellar chemistry
The abundances of elements in the Earth and the terrestrial planets provide the initial conditions for life and clues as to the history and formation of the Solar System. We follow the pioneering work of Bond et al. (2010) and combine…
We demonstrate a machine learning based approach which can learn the time-dependent electronic excitation dynamics of small molecules subjected to ion irradiation. Ensembles of recurrent neural networks are trained on data generated by…
Under cold conditions in dense cores, gas-phase molecules and atoms are depleted from the gas-phase to the surface of interstellar grains. Considering the time scales and physical conditions within these cores, a portion of these molecules…
Machine learning techniques including neural networks are popular tools for materials and chemical scientists with applications that may provide viable alternative methods in the analysis of structure and energetics of systems ranging from…
Astrochemical models are essential to bridge the gap between the timescales of reactions, experiments, and observations. Ice chemistry in these models experiences a large computational complexity as a result of the many parameters required…
While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here we introduce NEP89, a foundation model…
This paper presents the first substantial study of the chemistry of the envelopes around a sample of 18 low-mass pre- and protostellar objects for which physical properties have previously been derived from radiative transfer modeling of…
Galactic chemical abundances provide crucial insights into fundamental galactic parameters, such as the high-mass slope of the initial mass function (IMF) and the normalization of Type Ia supernova (SN Ia) rates. Constraining these…
Sulfur is one of the most abundant elements in the Universe, yet the sulfur budget inferred from the observed sulfur-bearing molecules in dense cores is significantly lower than expected. Starless and pre-stellar cores represent the…
A key obstacle to accurate models of the first stars and galaxies is the vast range of distance scales that must be considered. While star formation occurs on sub-parsec scales within dark matter (DM) minihalos, it is influenced by…
Context. High-resolution spectroscopy in the near-infrared (NIR) is a powerful tool for characterising the physical and chemical properties of cool-star atmospheres. The current generation of NIR echelle spectrographs enables the sampling…
We compare an evolutionary chemical model with simple empirical models of the abundance and with static chemical models. We focus on the prediction of molecular line profiles that are commonly observed in low mass star forming cores. We…
We investigate how the constraints on cosmological and astrophysical parameters ($\Omega_{\rm m}$, $\sigma_{8}$, $A_{\rm SN1}$, $A_{\rm SN2}$) vary when exploiting information from multiple fields in cosmology. We make use of a…
Aims: Interstellar molecules form early in the evolutionary sequence of interstellar material that eventually forms stars and planets. To understand this evolutionary sequence, it is important to characterize the chemical composition of its…
Particle-Mesh (PM) codes are still very useful tools for testing predictions of cosmological models in cases when extra high resolution is not very important. We release for public use a cosmological PM N-body code. We provide a complete…
Astrochemistry has been widely developed as a power tool to probe physical properties of the interstellar medium (ISM) in various conditions of the Milky Way (MW) Galaxy, and in near and distant galaxies. Most current studies conventionally…
The potential number of drug like small molecules is estimated to be between 10^23 and 10^60 while current databases of known compounds are orders of magnitude smaller with approximately 10^8 compounds. This discrepancy has led to an…
In astrophysics, solving complex chemical reaction networks is essential but computationally demanding due to the high dimensionality and stiffness of the ODE systems. Traditional approaches for reducing computational load are often…
High-fidelity quantum dynamics emulators can be used to predict the time evolution of complex physical systems. Here, we introduce an efficient training framework for constructing machine learning-based emulators. Our approach is based on…
Observations of CO+ suggest column densities on the order 10^12 cm^-2 that can not be reproduced by many chemical models. CO+ is more likely to be destroyed than excited in collisions with hydrogen. An anomalous excitation mechanism may…