Computationally efficient characterization of potential energy surfaces based on fingerprint distances

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This method allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling. Published by AIP Publishing

Impact of atmospheric emissions from ships in port on urban areas

Maritime transport, despite is one of the most efficient modes of transport, causes several major environmental problems, particularly about air pollution. Furthermore, due to the growth of the shipping activities, its environmental impact will be more relevant in the future. Because of the current environmental issues, the legislation on shipping emissions is frequently updated and becomes more and more stringent. Several policies were issued in the last decades, mainly focused on reducing the content of sulfur in marine fuels. The last international IMO-2020 regulation was enforced on 1 January 2020, it limits the sulfur content of any fuel oil used on board ships at a maximum of 0.50% m/m (mass by mass) outside emission control areas (ECAs) and 0.1% inside ECAs. Since 1 January 2021, in the Baltic and North Sea a nitrogen emission control area (NECA) was applied, requiring the ships built after 2021 operating in this area must respect a mandatory Tier 3 standard (80% reduction compare to Tier 1). The establishment of ECAs has become an important measure to reduce and control ship emissions. Even though, in-port emissions account for a relatively small proportion of the total emissions due to shipping, they can represent a significant impact on the health of population living in port cities and coastal areas. To accurately quantify the risk associated with atmospheric emissions of ships when in-port and the potential benefits of control measures, it is necessary to improve the state-of-art of both the estimation of emissions and the performance of atmospheric dispersion models. The aim of this thesis is to assess the impact of ship emissions in the port of Naples in 2018 by the development of a bottom-up procedure improving the state-of-art of present methodologies. Pollutants considered are NOx, SO2 and PM10. The developed methodology can be applied to any port. The first issue analysed in this thesis is the validation and the optimization of CALPUFF model when used to simulate the dispersion of the ship emissions in a port. With this aim wind tunnel tests and Computational Fluid Dynamics (CFD) numerical simulations were used to model the dispersion of atmospheric emissions of cruise ships at hoteling in the port of Naples. A part of the Naples urban area large about 1.2 km2 was reproduced at a scale of 1:500 for the wind tunnel experiments. The worst, but very frequent, emission scenario with three cruise ships emitting at the same time and wind blowing from the south-east with a speed at funnel height of 3 m/s in neutral stability conditions of the atmospheric boundary layer was studied. Two different values UR=1 and UR=4 of the ratio funnel gas velocity/wind speed were considered. In the wind tunnel experiments, Ethane was used as the tracer gas and its concentration was measured at 35 receptor points inside the urban area and at different heights. The dispersion of ship emissions in the same area was also studied by CFD simulations using steady-state solutions of the RANS equations with a k-ω shear-stress transport (SST) turbulence model. A very good agreement between wind tunnel and CFD results is observed. The same simulations were then performed with CALPUFF. The results were used to analyse the accuracy of the predictions of the dispersion model CALPUFF. The effect of two CALPUFF model options was studied: the building downwash module and the parameterisation of the dispersion coefficients. The CALPUFF results are less accurate than the CFD simulations and show a general tendency to underestimate the experimental data. However, the optimization process improves the performance of CALPUFF. A more comprehensive analysis of the effect of a varying UR in the range 0.25 – 16 was also undertaken using numerical models. The second step was the creation of a comprehensive global data base of all ships visiting the port of Naples in 2018. Using AIS data a data base containing the main information of more than 900 ships (category, name, IMO number, gross tonnage, deadweight; length, width, draft, total power installed onboard and the type of engines; maximum speed; the number of passengers, cars, containers) was created. All ships were lumped in 45 categories and five macro-categories (Commercial, Fishery, Passenger, Tanker, and Other). To fill the missing data, regressions based on real data for each category were adopted. Once created this "static" database, AIS data were processed through a MATLAB code which is able to identify the phase of ships on the basis of the analysis of the temporal data, of the time delta between records, and of the speed data. The phases defined are as follows: entry to the port, navigation in the port, the start, stop, and end phases of mooring at the quay, exit from the port, and engine start and stop. In this way at each AIS record is associated a specific activity-phase of the ship. Once all this information was completed, emission rates of NOx, SO2, and PM were calculated. Great accuracy was applied to the evaluation of the real power of main engines starting from the average speeds when ships are moving in port with respect to that using the typical load factors corresponding to the cruise phase. Similar accuracy was also applied to the evaluation of the total power of auxiliary engines both during the navigation and hoteling phases. The reference adopted is the recent EMEP/EEA guideline using specific emission factors defined for each category of ships and activity phase. AIS data were also used to identify in and out routes and mooring piers for each ship category. For the sake of simplicity piers and routes were merged when very close each other. In parallel to this approach, typical of bottom-up procedures, a statistical study based on data from 38 ports all over the world in 45 annualities was performed with the aim to correlate emissions with traffic data per year. Traffic data considered are: number of passengers, hours spent in each phase, number of calls for passenger ships; and tons of good, hours spent in each phase and number of calls for commercial ships. The correlation with traffic data gives the possibility of an easy check of the emissions estimated but show, as expected, a certain degree of uncertainty. Once verified and optimized the accuracy of CALPUFFF simulations, the complete emission inventory developed for the port of Naples was used as input to CALPUFF together with meteorological data. The results of simulations were compared with data from fixed monitoring stations in the urban area both as annual average and percentile. In this way the impact of ship emissions on air quality in Naples in 2018 was assessed. The research provides at the same time useful insights on the contribution of ship emissions to air pollution in Naples and on an accurate procedure to assess the impact of ship emissions in port cities

Proceedings of the XIII Global Optimization Workshop: GOW'16

[Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GO’99, 1999), Hanmer Springs (Let’s GO, 2001), Santorini (Frontiers in GO, 2003), San José (Go’05, 2005), Mykonos (AGO’07, 2007), Skukuza (SAGO’08, 2008), Toulouse (TOGO’10, 2010), Natal (NAGO’12, 2012) and Málaga (MAGO’14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOW’16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOW’16 would not have been possible without the valuable contribution from the authors and the International Scientific Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...

DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction

Protein (receptor)--ligand interaction prediction is a critical component in computer-aided drug design, significantly influencing molecular docking and virtual screening processes. Despite the development of numerous scoring functions in recent years, particularly those employing machine learning, accurately and efficiently predicting binding affinities for protein--ligand complexes remains a formidable challenge. Most contemporary methods are tailored for specific tasks, such as binding affinity prediction, binding pose prediction, or virtual screening, often failing to encompass all aspects. In this study, we put forward DeepRLI, a novel protein--ligand interaction prediction architecture. It encodes each protein--ligand complex into a fully connected graph, retaining the integrity of the topological and spatial structure, and leverages the improved graph transformer layers with cosine envelope as the central module of the neural network, thus exhibiting superior scoring power. In order to equip the model to generalize to conformations beyond the confines of crystal structures and to adapt to molecular docking and virtual screening tasks, we propose a multi-objective strategy, that is, the model outputs three scores for scoring and ranking, docking, and screening, and the training process optimizes these three objectives simultaneously. For the latter two objectives, we augment the dataset through a docking procedure, incorporate suitable physics-informed blocks and employ an effective contrastive learning approach. Eventually, our model manifests a balanced performance across scoring, ranking, docking, and screening, thereby demonstrating its ability to handle a range of tasks. Overall, this research contributes a multi-objective framework for universal protein--ligand interaction prediction, augmenting the landscape of structure-based drug design

Development of a tool for Bayesian data analysis and its application in Monte Carlo tuning

In this thesis, a novel approach to Monte Carlo event generator tuning, grounded in Bayesian reasoning, is presented. The Bayesian Analysis Toolkit (BAT.jl) is introduced as a modern tool for performing Bayesian inference. A numerical test suite that verifies the validity and performance of the BAT.jl package is developed. The test suite is used to evaluate the performance of the Markov chain Monte Carlo (MCMC) sampling algorithms implemented in BAT.jl, utilizing a selection of test functions and different metrics to quantify the quality of the samples. The results show that the MCMC algorithms are able to sample the posterior distributions of the test functions accurately. Utilizing the BAT.jl toolkit, two hadronization models within the Herwig Monte Carlo event generator (MCEG) are successfully tuned to data from the LEP experiments. Several aspects of the tuning procedure are investigated, such as parameter and observable selection and parametrization quality. Samples generated using the tuned parameters, obtained from the global mode of the posterior, are compared to data through a χ2 test. The resulting p-values for the tuned simulations significantly outperform those from the nominal MCEG samples, indicating a successful tune and an improved description of the data. The posterior is also used to present a method for propagating the parameter uncertainties to the realm of the observables, providing a measure for the tuning uncertainty. Studies on the impact of assigning weights to the observables and the impact of correlations between measurements on the tuning are also presented. These show that weights can alter the tuning results, especially in cases with multiple modes in the posterior. However, their influence on the quality of the tune is minimal in this case. The correlation of measurements has less of an impact on the position of the global mode but substantially affects the associated parameter uncertainties estimates. Finally, a comparison of the two tuned hadronization models is presented, which indicates that the Lund string model describes the data slightly better than the cluster hadronization model for this set of observables

Theoretical Investigation on the Biomolecular Systems using Multiscale Modelling

Die Untersuchung von Protein-Ligand-Wechselwirkungen ist für biomolekulare Systeme von entscheidender Bedeutung und eine Herausforderung. Insbesondere haben traditionelle Laborexperimente oft Schwierigkeiten, die Mechanismen der Reaktionen zu erklären, während klassische theoretische Berechnungsmethoden Defizite im Umgang mit der System- und Zeitskala biomolekularer Systeme aufweisen. In dieser Arbeit werden sogenannte enhanced Sampling-Methoden auf der Grundlage von Molekulardynamiksimulationen (MD) und Algorithmen für künstliche neuronale Netze (ANN), die auf semi-empirischen quantenmechanischen (QM) Ansätzen beruhen, zur Untersuchung verschiedener biomolekularer Systeme eingesetzt. Im ersten Teil wurde die Wirt-Gast-Chemie von [4+4]- und [2+3]-Iminkäfigen untersucht. Bei der Untersuchung von [4+4]-Käfigen wurde der Aufnahmeprozess von unterschiedlich großen Ammoniumionen in Käfigen mit alternativen Volumina durch wohltemperierte Metadynamik (MetaD) simuliert. Es wurden drei mögliche Mechanismen vorgeschlagen, um die Gastaufnahmeprozesse zu erklären. Bei der Untersuchung von [2+3]-Käfigen wurde der Stickstoffmolekültransfer in drei verschiedenen Käfigkristallen mit Funnel-Metadynamik (FM) berechnet. Die erhaltenen freien Energieflächen deuten auf die Existenz von zwei möglichen Wegen hin, auf denen der Stickstofftransfer erfolgen kann. Im zweiten Teil wurde eine neuartige Fluoreszenzsonde auf der Basis eines Glukose bindenden Proteins untersucht. Ein detailliertes molekulares Verständnis der Veränderungen an der Glukosebindestelle aufgrund von Mutationen und deren Auswirkungen auf die Glukosebindung wurde durch MD-Simulationen erreicht. Die Energetik der Dissoziation von Protein und Glukose wurde aufgedeckt und stimmte mit den experimentellen Ergebnissen überein. Schließlich wurde eine Reihe von künstlichen neuronalen Netzen (ANNs) trainiert, um die falsche Darstellung von angeregten Zuständen durch LC-DFTB zu korrigieren, wenn Energieniveaus kreuzen. Die meisten der trainierten Maschinen sind in der Lage, die durch LC-DFTB verursachten Fehler bei der Beschreibung des angeregten Zustands zuverlässig zu korrigieren, während die für Farbstoffgeometrien in Wasser trainierte Maschine weniger genaue Ergebnisse liefert und weiteres Training erfordert

Energy Barriers and Activated Dynamics in a Supercooled Lennard-Jones Liquid

We study the relation of the potential energy landscape (PEL) topography to relaxation dynamics of a small model glass former of Lennard-Jones type. The mechanism under investigation is the hopping betweem superstructures of PEL mimima, called metabasins (MB). From the mean durations \tauphi of visits to MBs, we derive effective depths of these objects by the relation \Eapp=\d\ln\tauphi/\d\beta, where \beta=1/\kB T. Since the apparent activation energies \Eapp are of purely dynamical origin, we look for a quantitative relation to PEL structure. A consequence of the rugged nature of MBs is that escapes from MBs are not single hops between PEL minima, but complicated multi-minima sequences. We introduce the concept of return probabilities to the bottom of MBs in order to judge whether the attraction range of a MB was left. We then compute the energy barriers that were surmounted. These turn out to be in good agreement with the effective depths \Eapp, calculated from dynamics. Barriers are identified with the help of a new method, which accurately performs a descent along the ridge between two minima. A comparison to another method is given. We analyze the population of transition regions between minima, called basin borders. No indication for the mechanism of diffusion to change around the mode-coupling transition can be found. We discuss the question whether the one-dimensional reaction paths connecting two minima are relevant for the calculation of reaction rates at the temperatures under study.Comment: 17 pages, 16 figure