We propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au 25 nanocluster, are utilized in our model. One advantage to a machinelearning approach is that correlations in defined features disentangle relationships among the various structural parameters. For example, in Au 25 , we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au 36 and Au 133 nanoclusters.

Doping metal nanoclusters with a second type of metal is a powerful method for tuning the physicochemical properties of nanoclusters at the atomic level and it also provides opportunities for a fundamental understanding of alloying rules as well as new applications. Herein, we have devised a new, one-phase strategy for achieving heavy Ag-doping in Au(SR) nanoclusters. This strategy overcomes the light doping of silver by previous methods. X-ray crystallography together with ESI-MS determined the composition of the product to be [AgAu(SCH)] with x ∼ 21. Cryogenic optical spectroscopy (80-300 K) revealed fine features in optical absorption peaks. Interestingly, the heavy doping of silver does not significantly change the electron-phonon coupling strength and the surface phonon frequency. DFT simulations reproduced the experimentally observed trend of electronic structure evolution with Ag doping. We further investigated the electrocatalytic performance of such heavily Ag-doped nanoclusters for oxygen reduction in alkaline solutions. The mass activity of ligand-off [AgAu(SCH)] nanoclusters (217.4 A g) was determined to be higher than that of ligand-on nanoclusters (29.6 A g) at a potential of -0.3 V (vs. Ag/AgCl). The rotating disk electrode (RDE) studies revealed the tunable kinetic features of the nanoclusters by ligand removal.

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system strings, molecular graphs, and three-dimensional atomic coordinates using four different neural network architectures—fully connected neural networks, recurrent neural networks, graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about the molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.

Gold nanoparticles distinguish themselves from other nanoparticles due to their unique surface plasmon resonance properties that can be exploited for a multiplicity of applications. The promise of plasmonic heating in systems of Au nanoparticles on transition metal oxide supports, for example, Au/TiO2, rests with the ability of the surface plasmon in Au nanoparticles to effectively transfer energy into the transition metal oxide. Here, we report a critical observation regarding Au nanoparticle (Au55) surface plasmon excitations, that is, the relaxation of the surface plasmon excitation is very slow, on the order of several picoseconds. Starting from five plasmon states in Au55 nanoparticles using nonadiabatic molecular dynamics simulations, we find that the relaxation time constant resulting from these simulations is ∼6.8 ps, mainly resulting from a long-lived intermediate state found at around -0.8 eV. This long-lived intermediate state aligns with the conduction band edge of TiO2, thereby facilitating energy transfer injection from the Au55 nanoparticle into the TiO2. The current results rule out the previously reported molecular-like relaxation dynamics for Au55.

In this study, we explore the structural, electronic and catalytic properties of bimetallic nanoparticles of the form Au25-xAgx(SR)18 (for x = 6, 7, 8). Due to the combinatorial enormity of the number of different alloyed structures, we choose 500 random configurations corresponding to each alloying level and energetically optimize their structures. Here we report the properties of the lowest energy structures and determine the most favorable Ag alloying sites for these systems. We also show that nanoalloys with one Ag at the center and the rest in the outer shell of the Au13 kernel are less energetically favorable than the ones with all the Ag atoms occupying the surface of the Au13 kernel. We further present experimental results showing that catalytic oxidation of CO is adversely affected due to Ag alloying. We provide qualitative and quantitative evidence to explain this reduction of the catalytic activity using Fukui functions and average adsorption energies respectively.

Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

We present compelling experimental results of the optical characteristics of transparent oxide CuGaO2 and related CuGa1-xFexO2 (with 0.00≤x≤0.05) alloys, whereby the forbidden electronic transitions for CuGaO2 become permissible in the presence of B-site (Ga sites) alloying with Fe. Our computational structural results imply a correlation between the global strain on the system and a decreased optical absorption edge. However, herein, we show that the relatively ordered CuGa1-xFexO2 (for 0.00≤x≤0.04) structures exhibit much weaker vis-absorption compared to the relatively disordered CuGa0.95Fe0.05O2.

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