相关论文: Machine Learning interpretation of the correlation…
Machine learning feature importance calculations are used to determine the molecular substructures that are responsible for mid and far-infrared (IR) emission features of neutral polycyclic aromatic hydrocarbons (PAHs). Using the…
We design and train a neural network (NN) model to efficiently predict the infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a computational cost many orders of magnitude lower than what a first-principles…
The observed variations in profiles of the interstellar aromatic infrared bands correlate with the object type and are indicative of PAH populations existing i n different sources. Spectroscopic studies on PAHs can provide tools for the int…
We introduce a machine learning molecular dynamics (MLMD) approach to calculate the anharmonic infrared (IR) absorption spectra of polycyclic aromatic hydrocarbons (PAHs), key carriers of interstellar aromatic IR bands. This method accounts…
The Interest in polycyclic aromatic hydrocarbons (PAHs) spans numerous fields and infrared spectroscopy is usually the method of choice to disentangle their molecular structure. In order to compute vibrational frequencies, numerous…
The mid-infrared (IR; 5-15~$\mu$m) spectrum of a wide variety of astronomical objects exhibits a set of broad emission features at 6.2, 7.7, 8.6, 11.3 and 12.7 $\mu$m. About 30 years ago it was proposed that these signatures are due to…
Polycyclic aromatic hydrocarbon (PAH) molecules are widely considered as the preferred candidate for the carrier of the unidentified infrared emission bands observed in the interstellar medium and circumstellar envelopes. In this paper we…
In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in the presence of i) high correlation between the predictors…
In the era of high-sensitivity infrared (IR) astronomy, traditional manual diagnostics are no longer sufficient to harvest the complex physical insights hidden within interstellar spectra. We introduce a machine learning paradigm that…
Studying the anharmonicity in the infrared (IR) spectra of polycyclic aromatic hydrocarbons (PAHs) at elevated temperatures is important to understand vibrational features and chemical properties of interstellar dust, especially in the…
Polycyclic Aromatic Hydrocarbons (PAHs) are considered as a major constituent of interstellar dust. They have been proposed as the carriers of the Aromatic Infrared Bands (AIBs) observed in emission in the mid-IR. They likely have a…
Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes…
Accurately and efficiently predicting the infrared (IR) spectra of a molecule can provide insights into the structure-properties relationships of molecular species, which has led to a proliferation of machine learning tools designed for…
We decompose the observed 7.7 $\mu$m polycyclic aromatic hydrocarbon (PAH) emission complexes in a large sample of over 7000 mid-infrared spectra of the interstellar medium (ISM) using spectral cubes observed with the Spitzer/IRS-SL…
Interstellar space hosts nanometre- to micron-sized dust grains. The carbonaceous-rich component of these grain populations emits in infrared bands, observed remotely for decades with telescopes and satellites. They are a key ingredient of…
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…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
In chemical processing and bioprocessing, conventional online sensors are limited to measure only basic process variables like pressure and temperature, pH, dissolved O and CO$_2$ and viable cell density (VCD). The concentration of other…
Astrochemical modelling of the interstellar medium typically makes use of complex computational codes with parameters whose values can be varied. It is not always clear what the exact nature of the relationship is between these input…
Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning algorithm with…