Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks.

The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN) architecture to train and model the potential energy surface of molecular clusters relevant to atmospheric new particle formation, such as sulfuric acid-ammonia clusters. We compare the differences between PaiNN and previous kernel ridge regression modeling for the Clusteromics I-V data sets. We showcase three models capable of predicting electronic binding energies and interatomic forces with mean absolute errors of <0.3 kcal mol-1 and <0.2 kcal mol-1 Å-1, respectively. Furthermore, we demonstrate that the error of the modeled properties remains below the chemical accuracy of 1 kcal mol-1 even for clusters vastly larger than those in the training database (up to (H2SO4)15(NH3)15 clusters, containing 30 molecules). Consequently, we emphasize the potential applications of these models for faster and more thorough configurational sampling and for boosting molecular dynamics studies of large atmospheric molecular clusters.

A machine learning potential for simulating infrared spectra of nanosilicate clusters.

The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here, we apply an accurate and efficient protocol to collect training data for constructing a neural network-based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium and in circumstellar environments.

Atomistic structure search using local surrogate model.

We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential formalism and is based on the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch k-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic systems, including molecules, nanoparticles, surface supported clusters, and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to benefit from transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches.

Dimerization of dehydrogenated polycyclic aromatic hydrocarbons on graphene.

Dimerization of polycyclic aromatic hydrocarbons (PAHs) is an important, yet poorly understood, step in the on-surface synthesis of graphene (nanoribbon), soot formation, and growth of carbonaceous dust grains in the interstellar medium (ISM). The on-surface synthesis of graphene and the growth of carbonaceous dust grains in the ISM require the chemical dimerization in which chemical bonds are formed between PAH monomers. An accurate and cheap method of exploring structure rearrangements is needed to reveal the mechanism of chemical dimerization on surfaces. This work has investigated the chemical dimerization of two dehydrogenated PAHs (coronene and pentacene) on graphene via an evolutionary algorithm augmented by machine learning surrogate potentials and a set of customized structure operators. Different dimer structures on surfaces have been successfully located by our structure search methods. Their binding energies are within the experimental errors of temperature programmed desorption measurements. The mechanism of coronene dimer formation on graphene is further studied and discussed.

Chemically-resolved determination of hydrogenated graphene-substrate interaction.

Functionalization of graphene on Ir(111) is a promising route to modify graphene by chemical means in a controlled fashion at the nanoscale. Yet, the nature of such functionalized sp3 nanodots remains unknown. Density functional theory (DFT) calculations alone cannot differentiate between two plausible structures, namely true graphane and substrate stabilized graphane-like nanodots. These two structures, however, interact dramatically differently with the underlying substrate. Discriminating which type of nanodots forms on the surface is thus of paramount importance for the applications of such prepared nanostructures. By comparing X-ray standing wave measurements against theoretical model structures obtained by DFT calculations we are able to exclude the formation of true graphane nanodots and clearly show the formation graphane-like nanodots.

The Synergy between Metal Facet and Oxide Support Facet for Enhanced Catalytic Performance: The Case of Pd-TiO2.

The demand for catalyst with higher activity and higher selectivity is still a central issue in current material science community. On the basis of first-principles calculations, we demonstrate that the catalytic performance of the Pd-TiO2 hybrid nanostructures can be selectively promoted or depressed by choosing the suitable shaped Pd and TiO2 nanocrystals. To be more specific, the catalytic activities of Pd nanoparticles enclosed by (100) or (111) facets can be promoted more significantly when dosed on the TiO2(001) than on TiO2(101) under irradiation. Such theoretical prediction has then been further verified by the experimental observations in which the Pd(100)-TiO2(001) composites exhibit the highest catalytic performance toward the activation of oxygen among all the other shaped hybrid nanostructures. As a result, the selection of facets of support materials can provide an extra tuning parameter to control the catalytic activities of metal nanoparticles. This research opened up a new direction for designing and preparing catalysts with enhanced catalytic performance.

Structural Variation in Surface-Supported Synthesis by Adjusting the Stoichiometric Ratio of the Reactants.

Surface-supported coupling reactions between 1,3,5-tris(4-formylphenyl)benzene and aromatic amines have been investigated on Au(111) using scanning tunneling microscopy under ultra-high-vacuum conditions. Upon annealing to moderate temperatures, various products, involving the discrete oligomers and the surface covalent organic frameworks, are obtained through thermal-triggered on-surface chemical reactions. We conclude from the systematic experiments that the stoichiometric composition of the reactants is vital to the surface reaction products, which is rarely reported so far. With this knowledge, we have successfully prepared two-dimensional covalently bonded networks by optimizing the stoichiometric proportions of the reaction precursors.

Tissue-specific metabolic responses of Cyprinus flammans to copper.

Copper (Cu) contamination is serious in China, with ≤2.76 mg/L in some waters. Exposure to Cu causes a high toxicity to the aquatic organisms and subsequent ecological risk. To understand fish responses to Cu exposure, we analyzed the metabonomic changes in multiple tissues (gill, liver, and muscle) of Cyprinus flammans using an nuclear magnetic resonance-based metabonomic technique. Our results showed that metabolic alterations are dose-dependent. No significant metabolic alterations in three tissues of fish are caused by 0.25 mg/L Cu. However, 1.53 mg/L Cu caused changes of energy-related metabolites and amino acids, which we suggest are due to enhanced metabolic acidosis in gill and muscle, decreased tricarboxylic acid cycle activity in muscle, increased gluconeogenesis from amino acids in liver, and improved glycogenesis in liver and muscle. The Cori cycle between liver and muscle is concurrently triggered. Furthermore, high concentration of Cu resulted in the alteration of choline metabolism such that we hypothesize that Cu induces membrane damage and detoxification of CuSO4 in gill as well as altered osmoregulation in all three tissues. Choline-O-sulfate in gill may be used as a biomarker to provide an early warning of Cu exposure in C. flammans. Moreover, Cu exposure caused alterations of nucleoside and nucleotide metabolism in both gill and muscle. These findings provide a new insight into the metabolic effects of Cu exposure on C. flammans and highlight the value of metabonomics in the study of metabolic metal disturbance in fish.