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Precursor chemistry of h-BN: adsorption{,} desorption{,} and decomposition of borazine on Pt(110)
Published in Physical Chemistry Chemical Physics, 2020
Adsorption, desorption and fragmentation of borazine on Pt(110) are studied by temperature-programmed desorption, ultraviolet photoemission spectroscopy, workfunction measurements and density functional theory. Borazine adsorbs in part dissociatively, forming an upright (B3N3H5)ads adsorption complex. Radicals with a N–Pt bond are weakly bound and desorb recombinatively following second-order kinetics. Radicals with a B–Pt bond are similar in binding strength to the molecularly adsorbed species, which binds through dispersive forces to the (111) facets of the (1 × 2) reconstructed Pt(110). Both do not desorb but are dehydrogenated beyond T = 150 K. As T approaches 600 K the B–N ring progressively breaks down into its atomic constituents. The borazine ice multilayer is capable of trapping significant amounts of hydrogen. Previous studies of borazine adsorption on other transition metal surfaces yield a very similar pattern. Reported multiple molecular desorption peaks are artefacts. Implications for the nucleation and growth of h-BN monolayers at high temperatures are discussed.
Influence of Strain on Acid–Basic Properties of Oxide Surfaces
Published in The Journal of Physical Chemistry C, 2020
Adsorption, desorption and fragmentation of borazine on Pt(110) are studied by temperature-programmed desorption, ultraviolet photoemission spectroscopy, workfunction measurements and density functional theory. Borazine adsorbs in part dissociatively, forming an upright (B3N3H5)ads adsorption complex. Radicals with a N–Pt bond are weakly bound and desorb recombinatively following second-order kinetics. Radicals with a B–Pt bond are similar in binding strength to the molecularly adsorbed species, which binds through dispersive forces to the (111) facets of the (1 × 2) reconstructed Pt(110). Both do not desorb but are dehydrogenated beyond T = 150 K. As T approaches 600 K the B–N ring progressively breaks down into its atomic constituents. The borazine ice multilayer is capable of trapping significant amounts of hydrogen. Previous studies of borazine adsorption on other transition metal surfaces yield a very similar pattern. Reported multiple molecular desorption peaks are artefacts. Implications for the nucleation and growth of h-BN monolayers at high temperatures are discussed.
Chemical Reactivity of Supported ZnO Clusters: Undercoordinated Zinc and Oxygen Atoms as Active Sites
Published in ChemPhysChem, 2020
The growth of ZnO clusters supported by ZnO-bilayers on Ag(111) and the interaction of these oxide nanostructures with water have been studied by a multi-technique approach combining temperature-dependent infrared reflection absorption spectroscopy (IRRAS), grazing-emission X-ray photoelectron spectroscopy, and density functional theory calculations. Our results reveal that the ZnO bilayers exhibiting graphite-like structure are chemically inactive for water dissociation, whereas small ZnO clusters formed on top of these well-defined, yet chemically passive supports show extremely high reactivity - water is dissociated without an apparent activation barrier. Systematic isotopic substitution experiments using H216O/D216O/D218O allow identification of various types of acidic hydroxyl groups. We demonstrate that a reliable characterization of these OH-species is possible via co-adsorption of CO, which leads to a red shift of the OD frequency due to the weak interaction via hydrogen bonding. The theoretical results provide atomic-level insight into the surface structure and chemical activity of the supported ZnO clusters and allow identification of the presence of under-coordinated Zn and O atoms at the edges and corners of the ZnO clusters as the active sites for H2O dissociation.
Relationship Between Prediction Accuracy and Uncertainty in Compound Potency Prediction Using Deep Neural Networks and Control Models
Published in Scientific Reports, 2024
The assessment of prediction variance or uncertainty contributes to the evaluation of machine learning models. In molecular machine learning, uncertainty quantification is an evolving area of research where currently no standard approaches or general guidelines are available. We have carried out a detailed analysis of deep neural network variants and simple control models for compound potency prediction to study relationships between prediction accuracy and uncertainty. For comparably accurate predictions obtained with models of different complexity, highly variable prediction uncertainties were detected using different metrics. Furthermore, a strong dependence of prediction characteristics and uncertainties on potency levels of test compounds was observed, often leading to over- or under-confident model decisions with respect to the expected variance of predictions. Moreover, neural network models responded very differently to training set modifications. Taken together, our findings indicate that there is only little, if any correlation between compound potency prediction accuracy and uncertainty, especially for deep neural network models, when predictions are assessed on the basis of currently used metrics for uncertainty quantification.
Machine learning models with distinct Shapley value explanations decouple feature attribution and interpretation for chemical compound predictions
Published in Cell Reports Physical Science, 2024
Explaining black box predictions of machine learning (ML) models is a topical issue in artificial intelligence (AI) research. For the identification of features determining predictions, the Shapley value formalism originally developed in game theory is widely used in different fields. Typically, Shapley values quantifying feature contributions to predictions need to be approximated in machine learning. We introduce a framework for the calculation of exact Shapley values for 4 kernel functions used in support vector machine (SVM) models and analyze consistently accurate compound activity predictions based on exact Shapley values. Dramatic changes in feature contributions are detected depending on the kernel function, leading to mostly distinct explanations of predictions of the same test compounds. Very different feature contributions yield comparable predictions, which complicate numerical and graphical model explanation and decouple feature attribution and human interpretability.
Protocol to calculate and compare exact Shapley values for different kernels in support vector machine models using binary features
Published in STAR Protocols, 2024
The Shapley value formalism from cooperative game theory was adapted to explain predictions of machine learning models. Here, we present a protocol to calculate and compare exact Shapley values for support vector machine models with commonly used kernels and binary input features. We describe steps for installing software, preparing data, and calculating Shapley values with customizable Python scripts. We then detail procedures for analyzing results via correlation analysis and feature mapping.
talks
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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