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Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.A high-nuclearity 3d-4f cluster of [Gd30CoII6CoIII6(OH)56(NO3)12(CH3COO)30(H2O)30]·(NO3)22·(en)3·(H2O)3 (1) was synthesized through the reaction of Gd(NO3)3·6H2O, Co(NO3)2·6H2O, and sodium acetate in a mixture of ethanediamine (en), ethanol, and deionized water. The cluster core in 1 features a double-shell structure with a Co12 icosahedron encapsulating a Gd30 icosidodecahedron. A magnetic study reveals that separating Co2+ ions with Gd3+ ions can effectively reduce the magnetic interaction of 3d-4f clusters. Significantly, the magnetocaloric effect (MCE) of 1 at 2 K and 7 T is up to 44.7 J kg-1 K-1, the largest MCE reported to date in the 3d-4f metal clusters.Transition-metal thiophosphates and selenophosphates are layered systems with the potential for displaying two-dimensional (2D) magnetic phenomena. We present the crystal structures and magnetic properties of two lithium transition-metal thiophosphates, Li1.56Co0.71P2S6 and Li2.26Fe0.94P2S6. The previously unreported Li1.56Co0.71P2S6 crystallizes in the trigonal space group P31m with lattice parameters a = 6.0193(6) Å and c = 6.5675(9) Å. The CoS6 octahedra are arranged in a honeycomb lattice and form 2D layers separated by lithium cations. The previously solved Li2.26Fe0.94P2S6 is isostructural to Li1.56Co0.71P2S6 but displays site mixing between the Li+ and Fe2+ cations within the thiophosphate layer. learn more Unusually, Li1.56Co0.71P2S6 appears to have P2S63- and not P2S64- anions. We therefore term it a “noninnocent” anion because of the ambiguous nature of its oxidation state. Combined neutron diffraction and magnetization measurements reveal that both Li1.56Co0.71P2S6 and Li2.26Fe0.94P2S6 display magnetic anisotropy as well as no long-range magnetic order down to 5 K. In the iron thiophosphate, susceptibility indicates an effective moment of 5.44(3) μB, which may be best described by an S + L model, where S = 2 and L = 2, or close to the free ion limit. In the cobalt thiophosphate, we found the effective moment to be 4.35(2) μB, which would point to an S = 3/2 and L = 1 model due to octahedral crystal-field splitting.The increasing demand for online sensors applied to advanced control strategies in water resource recovery facilities has resulted in the increasing investigation of fault-detection methods to improve the reliability of sensors installed in harsh environments. The study herein focuses on the fault detection of ammonium sensors, especially for effluent monitoring, given their potential in ammonium-based aeration control applications. An artificial neural network model was built to predict the ammonium content in the effluent by employing the information from five other sensors installed in the activated sludge tank NH4+, pH, ORP, DO, and TSS. The residual between the model prediction and the effluent ammonium sensor signal was utilized in a fault-detection mechanism based on principal component analysis and Shewhart monitoring charts. In contrast to previous studies, the present work utilizes typical faults collected from a 1 year historic dataset of an actual sensor setup. Treatment process anomalies, calibration bias faults, and fouling drifts were the most common issues identified from the historic dataset, and they were promptly identified by the proposed fault-detection methodology. Once a fault is detected, the model prediction can be actively used in place of the sensor for process control without affecting the treatment process by utilizing faulty datasets.Understanding potential health risks associated with biofuel production is critical to sustainably combating energy insecurity and climate change. However, the specific health impacts associated with biorefinery-related emissions are not yet well characterized. We evaluated the relationship between respiratory emergency department (ED) visits (2011-2015) and residential exposure to biorefineries by comparing 15 biorefinery sites to 15 control areas across New York (NY) State. We further examined these associations by biorefinery types (e.g., corn, wood, or soybean), seasons, and lower respiratory disease subtypes. We measured biorefinery exposure using residential proximity in a cross-sectional study and estimation of biorefinery emission via AERMOD-simulated modeling. After controlling for multiple confounders, we consistently found that respiratory ED visit rates among residents living within 10 km of biorefineries were significantly higher (rate ratios (RRs) range from 1.03 to 3.64) than those in control areas across our two types of exposure indices. This relationship held across biorefinery types (higher in corn and soybean biorefineries), seasons (higher in spring and winter), air pollutant types (highest for NO2), and respiratory subtypes (highest for emphysema). Further research is needed to confirm our findings.The new indium-based organic framework (Me2NH2)[In(BDPO)]·DMF·2H2On (1) was successfully constructed by using the oxalamide group modified ligand N,N’-bis(isophthalic acid)oxalamide (H4BDPO). This framework presents a 2-fold interpenetrating structural characteristic, and the unique polar pore environment leads to a high capture ability for CO2, C2Hn and CH3OH and good separation ability for CO2 and C2Hn over CH4 as well as for CH3OH over C2H5OH, which was further verified by an ideal adsorbed solution theory (IAST) calculation. Theoretical simulations pointed out the possible adsorption sites of different adsorbed gases in 1. In addition, the excellent chemical stability and strong luminescence of 1 give it an effective selective detection ability for 2,6-dichloro-4-nitroaniline (DCN) in water with a low detection limit of 3.85 ppm, and the detection mechanism is discussed in detail.