Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons: A Message Passing Neural Network Approach.

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 this purpose. However, earlier studies have focused primarily on obtaining normalized IR spectra, which limits their potential for a comprehensive analysis of molecular behavior in the IR range. For instance, to fully understand and predict the optical properties, such as the transparency characteristics, it is necessary to predict the molar absorptivity IR spectra instead.

Enablers and barriers to primary health care access for Indigenous adolescents: a systematic review and meta-aggregation of studies across Australia, Canada, New Zealand, and USA.

Background: Indigenous adolescents access primary health care services at lower rates, despite their greater health needs and experience of disadvantage. This systematic review identifies the enablers and barriers to primary health care access for Indigenous adolescents to inform service and policy improvements.

Methods: We systematically searched databases for publications reporting enablers or barriers to primary health care access for Indigenous adolescents from the perspective of adolescents, their parents and health care providers, and included studies focused on Indigenous adolescents aged 10-24 years from Australia, Canada, New Zealand, and United States of America.

Recent advances in the SISSO method and their implementation in the SISSO++ code.

Accurate and explainable artificial-intelligence (AI) models are promising tools for accelerating the discovery of new materials. Recently, symbolic regression has become an increasingly popular tool for explainable AI because it yields models that are relatively simple analytical descriptions of target properties. Due to its deterministic nature, the sure-independence screening and sparsifying operator (SISSO) method is a particularly promising approach for this application.

Anharmonicity in Thermal Insulators: An Analysis from First Principles.

The anharmonicity of atomic motion limits the thermal conductivity in crystalline solids. However, a microscopic understanding of the mechanisms active in strong thermal insulators is lacking. In this Letter, we classify 465 experimentally known materials with respect to their anharmonicity and perform fully anharmonic ab initio Green-Kubo calculations for 58 of them, finding 28 thermal insulators with κ<10  W/mK including 6 with ultralow κ≲1  W/mK.