Generation and structural characterization of Ge carbides GeCn ( n = 4, 5, 6) by laser ablation, broadband rotational spectroscopy, and quantum chemistry

International audienc

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1. A scalable machine learning workflow for materials science leveraging PyTorch Lightning, which enables seamless scaling across different computation capabilities (laptop, server, cluster) and hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for rapid graph neural network prototyping and development. By publishing and sharing this toolkit with the research community via open-source release, we hope to: 1. Lower the entry barrier for new machine learning researchers and practitioners that want to get started with the OpenCatalyst dataset, which presently comprises the largest computational materials science dataset. 2. Enable the scientific community to apply advanced machine learning tools to high-impact scientific challenges, such as modeling of materials behavior for clean energy applications. We demonstrate the capabilities of our framework by enabling three new equivariant neural network models for multiple OpenCatalyst tasks and arrive at promising results for compute scaling and model performance.Comment: Paper accompanying Open-Source Software from https://github.com/IntelLabs/matscim

Vibrational Satellites of C2_2S, C3_3S, and C4_4S: Microwave Spectral Taxonomy as a Stepping Stone to the Millimeter-Wave Band

We present a microwave spectral taxonomy study of several hydrocarbon/CS$_2$ discharge mixtures in which more than 60 distinct chemical species, their more abundant isotopic species, and/or their vibrationally excited states were detected using chirped-pulse and cavity Fourier-transform microwave spectroscopies. Taken together, in excess of 85 unique variants were detected, including several new isotopic species and more than 25 new vibrationally excited states of C$_2$S, C$_3$S, and C$_4$S, which have been assigned on the basis of published vibration-rotation interaction constants for C$_3$S, or newly calculated ones for C$_2$S and C$_4$S. On the basis of these precise, low-frequency measurements, several vibrationally exited states of C$_2$S and C$_3$S were subsequently identified in archival millimeter-wave data in the 253--280 GHz frequency range, ultimately providing highly accurate catalogs for astronomical searches. As part of this work, formation pathways of the two smaller carbon-sulfur chains were investigated using $^{13}$C isotopic spectroscopy, as was their vibrational excitation. The present study illustrates the utility of microwave spectral taxonomy as a tool for complex mixture analysis, and as a powerful and convenient `stepping stone' to higher frequency measurements in the millimeter and submillimeter bands.Comment: Accepted in PCC

Implementation of Rare Isotopologues into Machine Learning of the Chemical Inventory of the Solar-Type Protostellar Source IRAS 16293-2422

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 formed, the chemistry is known to be drastically different from that of largely quiescent dark clouds. To that end, we have tested the ability of various machine learning models to fit the column densities of the molecules detected in source B of the Class 0 protostellar binary IRAS 16293-2422. By including a simple encoding of isotopic composition in our molecular feature vectors, we also examine for the first time how well these models can replicate the isotopic ratios. Finally, we report the predicted column densities of the chemically relevant molecules that may be excellent targets for radioastronomical detection in IRAS 16293-2422B.Comment: Accepted for publication in Digital Discovery. 18 pages, 8 figures, 5 table

HSCO+^+ and DSCO+^+: a multi-technique approach in the laboratory for the spectroscopy of interstellar ions

Protonated molecular species have been proven to be abundant in the interstellar gas. This class of molecules is also pivotal for the determination of important physical parameters for the ISM evolution (e.g. gas ionisation fraction) or as tracers of non-polar, hence not directly observable, species. The identification of these molecular species through radioastronomical observations is directly linked to a precise laboratory spectral characterisation. The goal of the present work is to extend the laboratory measurements of the pure rotational spectrum of the ground electronic state of protonated carbonyl sulfide (HSCO$^+$) and its deuterium substituted isotopomer (DSCO$^+$). At the same time, we show how implementing different laboratory techniques allows the determination of different spectroscopical properties of asymmetric-top protonated species. Three different high-resolution experiments were involved to detected for the first time the $b-$type rotational spectrum of HSCO$^+$, and to extend, well into the sub-millimeter region, the $a-$type spectrum of the same molecular species and DSCO$^+$. The electronic ground-state of both ions have been investigated in the 273-405 GHz frequency range, allowing the detection of 60 and 50 new rotational transitions for HSCO$^+$ and DSCO$^+$, respectively. The combination of our new measurements with the three rotational transitions previously observed in the microwave region permits the rest frequencies of the astronomically most relevant transitions to be predicted to better than 100 kHz for both HSCO$^+$ and DSCO$^+$ up to 500 GHz, equivalent to better than 60 m/s in terms of equivalent radial velocity. The present work illustrates the importance of using different laboratory techniques to spectroscopically characterise a protonated species at high frequency, and how a similar approach can be adopted when dealing with reactive species.Comment: 7 pages, 4 figures. Accepted for publication in Astronomy and Astrophysic

Explaining the Chemical Inventory of Orion KL through Machine Learning

The interplay of the chemistry and physics that exists within astrochemically relevant sources can only be fully appreciated if we can gain a holistic understanding of their chemical inventories. Previous work by Lee et al. (2021) demonstrated the capabilities of simple regression models to reproduce the abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1), as well as provide abundance predictions for new candidate molecules. It remains to be seen, however, to what degree TMC-1 is a ``unicorn'' in astrochemistry, where the simplicity of its chemistry and physics readily facilitates characterization with simple machine learning models. Here we present an extension in chemical complexity to a heavily studied high-mass star forming region: the Orion Kleinmann-Low (Orion KL) nebula. Unlike TMC-1, Orion KL is composed of several structurally distinct environments that differ chemically and kinematically, wherein the column densities of molecules between these components can have non-linear correlations that cause the unexpected appearance or even lack of likely species in various environments. This proof-of-concept study used similar regression models sampled by Lee et al. (2021) to accurately reproduce the column densities from the XCLASS fitting program presented in Crockett et al. (2014).Comment: 14 pages; 6 figures, 1 table in the main text. 0 figures, 1 table in the appendix. Accepted for publication in The Astrophysical Journal. Molecular dataset for machine learning can be found in the Zenodo repository here: https://zenodo.org/record/767560

A rotational and vibrational investigation of phenylpropiolonitrile (C6_6H5_5C3_3N)

The evidence for benzonitrile (C$_6$H$_5$CN}) in the starless cloud core TMC-1 makes high-resolution studies of other aromatic nitriles and their ring-chain derivatives especially timely. One such species is phenylpropiolonitrile (3-phenyl-2-propynenitrile, C$_6$H$_5$C$_3$N), whose spectroscopic characterization is reported here for the first time. The low resolution (0.5 cm$^{-1}$) vibrational spectrum of C$_6$H$_5$C$_3$N} has been recorded at far- and mid-infrared wavelengths (50 - 3500 cm$^{-1}$) using a Fourier Transform interferometer, allowing for the assignment of band centers of 14 fundamental vibrational bands. The pure rotational spectrum of the species has been investigated using a chirped-pulse Fourier transform microwave (FTMW) spectrometer (6 - 18 GHz), a cavity enhanced FTMW instrument (6 - 20 GHz), and a millimeter-wave one (75 - 100 GHz, 140 - 214 GHz). Through the assignment of more than 6200 lines, accurate ground state spectroscopic constants (rotational, centrifugal distortion up to octics, and nuclear quadrupole hyperfine constants) have been derived from our measurements, with a plausible prediction of the weaker bands through calculations. Interstellar searches for this highly polar species can now be undertaken with confidence since the astronomically most interesting radio lines have either been measured or can be calculated to very high accuracy below 300 GHz.Comment: 7 figures, 4 tables. Accepted for publication in J. Mol. Spe