IA Scholar Query: Entropic Convergence of Random Batch Methods for Interacting Particle Diffusion.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgWed, 16 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Searching for Singlet Fission Candidates with Many-Body Perturbation Theory and Machine Learning
https://scholar.archive.org/work/f537ccm2rrekzi5auw2yqm2bxa
Singlet fission (SF) is a photophysical process where one singlet-state exciton converts into two triplet-state excitons. SF is considered as a possible approach to surpass the Shockley-Queisser limit and has started wide discussions in the past decade. However, commercialization of SF-based photovoltaics remains in incubation due to the lack of practical SF materials. To tackle this bottleneck, performing large-scale simulation and screening molecular materials database to search for SF candidates with promising properties is suggested. One of the decisive excitonic properties directing the fission process, the SF thermodynamic driving force can be calculated with the state-of-the-art, many-body perturbation theory (MBPT) under the GW approximation paired with Bethe-Salpeter equation (BSE). However, GW+BSE calculation is too cumbersome to be selected as the screening scheme for a database with tens of thousands of molecular crystals. Statistical inference is hence introduced to maximize the probability of discovering SF candidates with minimized computational cost. To realize this process, a hierarchical screening workflow incorporating materials science and machine learning (MatML Workflow) was designed and implemented.Xingyu Liuwork_f537ccm2rrekzi5auw2yqm2bxaWed, 16 Nov 2022 00:00:00 GMTEfficient Gradient Flows in Sliced-Wasserstein Space
https://scholar.archive.org/work/u7hdgrxk75ceropnsoroqosite
Minimizing functionals in the space of probability distributions can be done with Wasserstein gradient flows. To solve them numerically, a possible approach is to rely on the Jordan-Kinderlehrer-Otto (JKO) scheme which is analogous to the proximal scheme in Euclidean spaces. However, it requires solving a nested optimization problem at each iteration, and is known for its computational challenges, especially in high dimension. To alleviate it, very recent works propose to approximate the JKO scheme leveraging Brenier's theorem, and using gradients of Input Convex Neural Networks to parameterize the density (JKO-ICNN). However, this method comes with a high computational cost and stability issues. Instead, this work proposes to use gradient flows in the space of probability measures endowed with the sliced-Wasserstein (SW) distance. We argue that this method is more flexible than JKO-ICNN, since SW enjoys a closed-form differentiable approximation. Thus, the density at each step can be parameterized by any generative model which alleviates the computational burden and makes it tractable in higher dimensions.Clément Bonet, Nicolas Courty, François Septier, Lucas Drumetzwork_u7hdgrxk75ceropnsoroqositeTue, 15 Nov 2022 00:00:00 GMTNovel DNA carrier structures for protein detection and analysis in a nanopore sensing system
https://scholar.archive.org/work/zlek5q5ozjfzbpymnqiev5lyxu
The continued development of diagnostic and therapeutic techniques for biological samples requires robust single-molecule detection techniques. Protein assays that are currently commonly used, such as ELISA, do not fit the specificity and sensitivity requirements, and also involve significant sample processing. In short, they do not provide what is needed for biomarker qualification and quantification. One technique that has the potential to offer improved single-molecule detection is nanopore sensing. Nanopore sensing uses electrical voltage to drive molecules across a small pore, one that is either biological or solid state. For this research, as solid state pore at the end of a pipette, a nanopipette, is used. In this technique, the current is continuously monitored and as specific molecules, usually DNAs or proteins, cross through the pore (translocate) the current changes. As nanopore sensing is a single-molecule technique, it easily fulfils the sensitivity requirements. Unfortunately, on its own, it is not able to select for specific molecules, and this limitation, in addition to difficulties that are encountered with protein translocations, leads to the necessity of DNA carriers. DNA carriers are typically double-stranded DNAs that has been modified to bind specifically to certain proteins. The translocations of these DNA tethered protein molecules then look significantly different compared to DNA on its own, such that it is possible to select for them specifically. The distinguishing figure for these events is typically another peak inside these events, or rather a subpeak. This research investigates two DNA carriers, a DNA dendrimer and a DNA plasmid carrier. The DNA dendrimer has great potential due to its customisability and ability to perform multiplexed sensing. To form the dendrimer, Y-shaped DNAs, each made of three oligonucleotides, was combined in stoichiometric ratios. For the first generation (G1) dendrimer, four Y-shaped DNAs were combined. This G1 could have three protein binding sites or be [...]Alexandra Dias-Lalcaca, Joshua Edel, Aleksander Ivanovwork_zlek5q5ozjfzbpymnqiev5lyxuMon, 14 Nov 2022 00:00:00 GMTArtificial Intelligence for Multiscale Study of Rechargeable Batteries
https://scholar.archive.org/work/l2or4vm5fng47otjuaezxj7kme
Global warming is currently considered one of the most crucial challenges worldwide. The necessity of reducing greenhouse gas pollution requires the development and utilization of renewable technologies capable of storing and delivering clean energy. Rechargeable batteries represent a prominent solution to achieve this goal. However, various limitations still prevent these devices from a wide-spread application substituting the existing polluting energy resources. The improvement of rechargeable batteries could be achieved, for example, with the appropriate engineering of electrochemical, thermal, and mechanical processes occurring at the atomic, nano, and micro scales, while incorporating accurate monitoring of a battery's functionality integrated into dynamic systems at the macro scale. Metallic and high entropy alloy (HEA) nanoparticles (NPs) are of great interest in various lithium-based batteries like lithium-oxygen batteries (LOBs) due to their superior electrocatalytic properties. Thus, improving battery's constituent materials at the nanoscale is important because the understanding at the fundamental level yields better predictive capability, which allows optimal design and operation of new battery systems. The scope of my thesis is to contribute to the investigation of features of rechargeable batteries at different scales. At the nanoscale, metallic and HEA NPs have been investigated, while at the macroscale the state-of-charge (SOC) and state-of-health (SOH) in different types of battery electric vehicles (BEVs) have been studied. The recent advancement of artificial intelligence (AI) techniques, based on machine learning (ML) and deep learning (DL) algorithms, have been used in this research to perform different types of predictions on simulated data generated through xvii multi-physics simulations, using experimentally acquired data for a final physical validation. At the nanoscale, fully convolutional neural networks (FCNs), trained on simulated transmission electron microscopy (TEM) images, like hi [...]Marco Ragonework_l2or4vm5fng47otjuaezxj7kmeTue, 08 Nov 2022 00:00:00 GMTSolving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows
https://scholar.archive.org/work/t5vy2bcjozgbjohgvtsysghzse
Solving Fredholm equations of the first kind is crucial in many areas of the applied sciences. In this work we adopt a probabilistic and variational point of view by considering a minimization problem in the space of probability measures with an entropic regularization. Contrary to classical approaches which discretize the domain of the solutions, we introduce an algorithm to asymptotically sample from the unique solution of the regularized minimization problem. As a result our estimators do not depend on any underlying grid and have better scalability properties than most existing methods. Our algorithm is based on a particle approximation of the solution of a McKean--Vlasov stochastic differential equation associated with the Wasserstein gradient flow of our variational formulation. We prove the convergence towards a minimizer and provide practical guidelines for its numerical implementation. Finally, our method is compared with other approaches on several examples including density deconvolution and epidemiology.Francesca R. Crucinio, Valentin De Bortoli, Arnaud Doucet, Adam M. Johansenwork_t5vy2bcjozgbjohgvtsysghzseFri, 21 Oct 2022 00:00:00 GMTSilicide-based Josephson field effect transistors for superconducting qubits
https://scholar.archive.org/work/h2nn6wi5srbrnd7caz3gbsbly4
Scalability in the fabrication and operation of quantum computers is key to move beyond the NISQ era. So far, superconducting transmon qubits based on aluminum Josephson tunnel junctions have demonstrated the most advanced results, though this technology is difficult to implement with large-scale facilities. An alternative "gatemon" qubit has recently appeared, which uses hybrid superconducting/semiconducting (S/Sm) devices as gate-tuned Josephson junctions. Current implementations of these use nanowires however, of which the large-scale fabrication has not yet matured either. A scalable gatemon design could be made with CMOS Josephson Field-Effect Transistors as tunable weak link, where an ideal device has leads with a large superconducting gap that contact a short channel through high-transparency interfaces. High transparency, or low contact resistance, is achieved in the microelectronics industry with silicides, of which some turn out to be superconducting. The first part of the experimental work in this thesis covers material studies on two such materials: V_3Si and PtSi, which are interesting for their high T_c, and mature integration, respectively. The second part covers experimental results on 50 nm gate length PtSi transistors, where the transparency of the S/Sm interfaces is modulated by the gate voltage. At low voltages, the transport shows no conductance at low energy, and well-defined features at the superconducting gap. The barrier height at the S/Sm interface is reduced by increasing the gate voltage, until a zero-bias peak appears around zero drain voltage, which reveals the appearance of an Andreev current. The successful gate modulation of Andreev current in a silicon-based transistor represents a step towards fully CMOS-integrated superconducting quantum computers.Tom Doekle Vethaakwork_h2nn6wi5srbrnd7caz3gbsbly4Tue, 06 Sep 2022 00:00:00 GMTNúmero completo
https://scholar.archive.org/work/735uzo5iejcunc6fayhqezjbqm
Idiomas aceptados Castellano, portugués e inglés (lenguas de la publicación).Revista Prometeicawork_735uzo5iejcunc6fayhqezjbqmThu, 11 Aug 2022 00:00:00 GMTTargeted modifications: conformational flexibility and design of ionic liquids
https://scholar.archive.org/work/pqs5v5rqrbdypjc2bh5xy7m2lu
The versatility of ionic liquids is an extraordinary challenge and chance for the future of chemistry. To efficiently harness this huge potential, our understanding of ionic liquids must grow to a point where application-oriented design becomes possible. However, structure-property relationships and thus design elements are often obscured by confounding variables. Here we show how useful design elements can be extracted with a systematic approach combining theory and experiment. To this end, we used targeted modifications of ionic liquids to isolate the effect of one variable at a time, with a focus on the influence of ion conformational space. Preparatory ab initio simulations on a large number of cations and ions revealed excellent model systems which were then synthesised and characterised experimentally. Physicochemical measurements and experimental crystal structures were in excellent agreement with ab initio results, thus building a consistent picture of the conformational space of ions in ionic liquids. In particular, conformational space emerged as a key design element for macroscopic properties, with an impact comparable to fluorination and ion volume. The link between conformational flexibility on a molecular level and the macroscopic transport properties was further investigated by means of classical molecular dynamics simulations. Additional experimental studies on uncharged analogues of ionic liquids revealed the need for a dual approach to the design of ionic liquids as well as an upper limit to such simple design elements. Thus, this work not only provides a set of specific design elements, but also a versatile strategy for the design of ionic liquids in general.Frederik Philippi, Thomas Welton, Joshua Edel, Patricia Hunt, Imperial College Londonwork_pqs5v5rqrbdypjc2bh5xy7m2luThu, 11 Aug 2022 00:00:00 GMTCollective Proposal Distributions for Nonlinear MCMC samplers: Mean-Field Theory and Fast Implementation
https://scholar.archive.org/work/ekyqga2r3fh3ffx3uirqgs4bom
Over the last decades, various "non-linear" MCMC methods have arisen. While appealing for their convergence speed and efficiency, their practical implementation and theoretical study remain challenging. In this paper, we introduce a non-linear generalization of the Metropolis-Hastings algorithm to a proposal that depends not only on the current state, but also on its law. We propose to simulate this dynamics as the mean field limit of a system of interacting particles, that can in turn itself be understood as a generalisation of the Metropolis-Hastings algorithm to a population of particles. Under the double limit in number of iterations and number of particles we prove that this algorithm converges. Then, we propose an efficient GPU implementation and illustrate its performance on various examples. The method is particularly stable on multimodal examples and converges faster than the classical methods.Grégoire Clarté, Antoine Diez, Jean Feydywork_ekyqga2r3fh3ffx3uirqgs4bomWed, 03 Aug 2022 00:00:00 GMTAn Introduction to Modern Statistical Learning
https://scholar.archive.org/work/5lgf33wamrbsva3zmwtwjvfude
This work in progress aims to provide a unified introduction to statistical learning, building up slowly from classical models like the GMM and HMM to modern neural networks like the VAE and diffusion models. There are today many internet resources that explain this or that new machine-learning algorithm in isolation, but they do not (and cannot, in so brief a space) connect these algorithms with each other or with the classical literature on statistical models, out of which the modern algorithms emerged. Also conspicuously lacking is a single notational system which, although unfazing to those already familiar with the material (like the authors of these posts), raises a significant barrier to the novice's entry. Likewise, I have aimed to assimilate the various models, wherever possible, to a single framework for inference and learning, showing how (and why) to change one model into another with minimal alteration (some of them novel, others from the literature). Some background is of course necessary. I have assumed the reader is familiar with basic multivariable calculus, probability and statistics, and linear algebra. The goal of this book is certainly not completeness, but rather to draw a more or less straight-line path from the basics to the extremely powerful new models of the last decade. The goal then is to complement, not replace, such comprehensive texts as Bishop's Pattern Recognition and Machine Learning, which is now 15 years old.Joseph G. Makinwork_5lgf33wamrbsva3zmwtwjvfudeWed, 20 Jul 2022 00:00:00 GMTMicrofluidic polymer particle and capsule formation: from the molecular to the macroscale
https://scholar.archive.org/work/cewkxnotcbbalbhwupvczoxluu
In this work the physics underpinning polymer particle formation is explored, particularly probing Å to 100s of nm length scales with small angle neutron scattering (SANS) to macroscopic scales with microscopy. Two approaches are used to generate micron-sized particles from polymer solution droplets; droplet solvent extraction (DSE) concerns the introduction of an 'extraction' solvent, which removes the solvent from the polymer-containing droplet, to form a polymer-rich skin and kinetically arrest the shrinking particle. Secondly, ionic gelation occurs upon addition of multivalent ions to a polyelectrolyte solution droplet, which rapidly forms a gel-like front which propagates into the droplet. Micro- and milli-fluidic approaches are used to generate droplets and visualise and map the formation pathways. Chapter 1 introduces background theory and the motivation for investigating these formation pathways. In Chapter 2, SANS measurements of model polymer/solvent/non-solvent mixtures, designed to probe the DSE pathway, are presented. In Chapter 3, the impacts of molecular structure (chain length and chemical substitution), concentration and droplet size on the DSE pathway are established; demonstrated here for model poly(vinyl alcohol) (PVA) to yield particles with well-de fined shape, dimensions and internal microstructure. Chapter 4 presents a comparative study with FlashNanoPrecipitation (FNP); another approach which utilises rapid mixing of ternary mixtures of polymer, solvent and non-solvent under confinement to yield nanoparticles. Chapter 5 details a SANS study on the effect of added mono- and divalent salt on the solution structure of sodium carboxymethyl cellulose (NaCMC). The formation of microcapsules from NaCMC and, instead, trivalent Fe3+ is detailed in Chapter 6. Optical measurements reveal the frontal behaviour and kinetics of the gelation process. Chapter 7 details a SANS study of these 'gelation fronts' to probe the evolution of nanostructure within the gel. Chapter 8 presents brief concluding remar [...]William Nicholas Sharratt, João Cabral, Engineering And Physical Sciences Research Council, Procter & Gamble Comapnywork_cewkxnotcbbalbhwupvczoxluuThu, 30 Jun 2022 00:00:00 GMTA Platform for High-Bandwidth, Low-Noise Electrical Nanopore Sensing with Thermal Control
https://scholar.archive.org/work/fy37zthldzb2dfrd6pen7gmf3y
Solid-state nanopores are an emerging class of single-molecule detectors that provide information about molecular identity via the analysis of transient fluctuations in the ionic current flowing across a nanoscale pore in a thin membrane. The transport of biomolecules across a pore is a key step in nanopore-based sensing of DNA, RNA and proteins. The dynamics of biomolecular transport are complex and depend on the strength of many interactions, which can be tuned with temperature. However, temperature is rarely controlled during solid-state nanopore experiments because of the added electrical noise from the temperature control and measurement systems, greatly reducing the signal-to-noise ratio when detecting individual molecules. So far, the use of electric-based heating and cooling strategies has limited the recording bandwidth to the kHz range, restricting the studies to long polymers translocating via the pore relatively slowly. Yet, many molecules translocate through the pore orders of magnitude faster. This research presents the development and testing of an instrument to allow low-noise electrical recording of nanopore signals at MHz bandwidth as a function of temperature. Initial experiments using this custom-built instrument for the study of linear DNA polymers confirm previously observed translocation behaviours, while providing a higher temporal resolution. Overall results show that high-speed nanopore experiments are possible while controlling the temperature up to 70 °C, opening up exciting opportunities to study the unfolding of proteins toward single-molecule protein sequencing and the passage of DNA nanostructures for different bioassays. Future work will focus on realizing microfluidic flow cells and nanopore performance at higher temperature for longer recording times.Dmytro Lomovtsev, University, Mywork_fy37zthldzb2dfrd6pen7gmf3yMon, 20 Jun 2022 00:00:00 GMTDeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method
https://scholar.archive.org/work/aowi3rvcfnfsnl3c2p72uwf75q
We introduce the so called DeepParticle method to learn and generate invariant measures of stochastic dynamical systems with physical parameters based on data computed from an interacting particle method (IPM). We utilize the expressiveness of deep neural networks (DNNs) to represent the transform of samples from a given input (source) distribution to an arbitrary target distribution, neither assuming distribution functions in closed form nor a finite state space for the samples. In training, we update the network weights to minimize a discrete Wasserstein distance between the input and target samples. To reduce computational cost, we propose an iterative divide-and-conquer (a mini-batch interior point) algorithm, to find the optimal transition matrix in the Wasserstein distance. We present numerical results to demonstrate the performance of our method for accelerating IPM computation of invariant measures of stochastic dynamical systems arising in computing reaction-diffusion front speeds through chaotic flows. The physical parameter is a large Pecl\'et number reflecting the advection dominated regime of our interest.Zhongjian Wang, Jack Xin, Zhiwen Zhangwork_aowi3rvcfnfsnl3c2p72uwf75qSun, 19 Jun 2022 00:00:00 GMTMolecular Dynamics for Synthetic Biology
https://scholar.archive.org/work/tjd3uiulavgb5bvr5cf6af2ify
Synthetic biology is the field concerned with the design, engineering, and construction of organisms and biomolecules. Biomolecules such as proteins are nature's nano-bots, and provide both a shortcut to the construction of nano-scale tools and insight into the design of abiotic nanotechnology. A fundamental technique in protein engineering is protein fusion, the concatenation of two proteins so that they form domains of a new protein. The resulting fusion protein generally retains both functions, especially when a linker sequence is introduced between the two domains to allow them to fold independently. Fusion proteins can have features absent from all of their components; for example, FRET biosensors are fusion proteins of two fluorescent proteins with a binding domain. When the binding domain forms a complex with a ligand, its dynamics translate the concentration of the ligand to the ratio of fluorescence intensities via FRET. Despite these successes, protein engineering remains laborious and expensive. Computer modelling has the potential to improve the situation by enabling some design work to occur virtually. Synthetic biologists commonly use fast, heuristic structure prediction tools like ROSETTA, I-TASSER and FoldX, despite their inaccuracy. By contrast, molecular dynamics with modern force fields has proven itself accurate, but sampling sufficiently to solve problems accurately and quickly enough to be relevant to experimenters remains challenging. In this thesis, I introduce molecular dynamics to a structural biology audience, and discuss the challenges and theory behind the technique. With this knowledge, I introduce synthetic biology through a review of fluorescent sensors. I then develop a simple computational tool, Rangefinder, for the design of one variety of these sensors, and demonstrate its ability to predict sensor performance experimentally. I demonstrate the importance of the choice of linker with yet another sensor whose performance depends critically thereon. In chapter 6, I investigate the [...]Josh Mitchell, University, The Australian Nationalwork_tjd3uiulavgb5bvr5cf6af2ifyWed, 15 Jun 2022 00:00:00 GMTImproving control based importance sampling strategies for metastable diffusions via adapted metadynamics
https://scholar.archive.org/work/qlfgwnp6uvbilhswybfdsfvdc4
Sampling rare events in metastable dynamical systems is often a computationally expensive task and one needs to resort to enhanced sampling methods such as importance sampling. Since we can formulate the problem of finding optimal importance sampling controls as a stochastic optimization problem, this then brings additional numerical challenges and the convergence of corresponding algorithms might as well suffer from metastabilty. In this article, we address this issue by combining systematic control approaches with the heuristic adaptive metadynamics method. Crucially, we approximate the importance sampling control by a neural network, which makes the algorithm in principle feasible for high dimensional applications. We can numerically demonstrate in relevant metastable problems that our algorithm is more effective than previous attempts and that only the combination of the two approaches leads to a satisfying convergence and therefore to an efficient sampling in certain metastable settings.Enric Ribera Borrell, Jannes Quer, Lorenz Richter, Christof Schüttework_qlfgwnp6uvbilhswybfdsfvdc4Tue, 14 Jun 2022 00:00:00 GMTEntropic Convergence of Random Batch Methods for Interacting Particle Diffusion
https://scholar.archive.org/work/772wwyhyqzcphljp2pvf3au5na
We propose a co-variance corrected random batch method for interacting particle systems. By establishing a certain entropic central limit theorem, we provide entropic convergence guarantees for the law of the entire trajectories of all particles of the proposed method to the law of the trajectories of the discrete time interacting particle system whenever the batch size B ≫ (α n)^1/3 (where n is the number of particles and α is the time discretization parameter). This in turn implies that the outputs of these methods are nearly statistically indistinguishable when B is even moderately large. Previous works mainly considered convergence in Wasserstein distance with required stringent assumptions on the potentials or the bounds had an exponential dependence on the time horizon. This work makes minimal assumptions on the interaction potentials and in particular establishes that even when the particle trajectories diverge to infinity, they do so in the same way for both the methods. Such guarantees are very useful in light of the recent advances in interacting particle based algorithms for sampling.Dheeraj Nagarajwork_772wwyhyqzcphljp2pvf3au5naWed, 08 Jun 2022 00:00:00 GMTIntroduction to Semi-discrete Calculus
https://scholar.archive.org/work/7englcyod5ctflt6qihffqbqvu
The Infinitesimal Calculus explores mainly two measurements: the instantaneous rates of change and the accumulation of quantities. This work shows that scientists, engineers, mathematicians, and teachers increasingly apply another change measurements tool: functions' local trends. While it seems to be a special case of the rate (via the derivative sign), this work proposes a separate and favorable mathematical framework for the trend, called Semi-discrete Calculus.Amir Shacharwork_7englcyod5ctflt6qihffqbqvuWed, 01 Jun 2022 00:00:00 GMTKinetics of Disordered Proteins and their Interactions
https://scholar.archive.org/work/bidkw6bwyzbcdeheliyox37jui
Disordered proteins and regions are highly prevalent in the human proteome, and are often implicated in disease. However, methods to study these systems in detail are lacking, and the potential for thermodynamic and kinetic characterisation using experimental methods is limited. Molecular simulations and associated analysis methods have advanced to the point where investigating disordered proteins and their interactions with other (bio-)molecules on an atomistic scale is now possible. Amyloid-β 42 (Aβ42) is an aggregation-prone biomolecule implicated in Alzheimer's disease, and recent work has shown that small molecules can inhibit the aggregation by dynamically binding to the monomeric form of this disordered protein. In this work I performed long-timescale simulations of Aβ42 with and without the addition of small molecules, and analysed the kinetics of the system using a neural network and a probabilistic state definition. Without a small molecule, the system occupies several states and transitions occur on the range of microseconds. With the small molecule 10074-G5, the dominant disordered state increases in population, and transitions out of this state become slower. Additionally, the conformational entropy of the protein backbone is increased, with the small molecule forming nanosecond-lifetime π-stacking interactions with aromatic side chains. These findings are consistent with nuclear magnetic resonance experiments, and indicate the possibility of designing molecules with high specificity. Another approach to targeting aggregation prone proteins such as Aβ42 consists of using specially engineered single-domain antibodies (sdAbs) with a modified complementarity determining region (CDR). These complementarity determining regions (CDRs) are often disordered and their dynamics are poorly understood. I performed enhanced sampling simulations of both an sdAb designed using a sequence-matching method as well as one developed with a structural approach to better understand their conformational space and provide i [...]Thomas Lohr, Apollo-University Of Cambridge Repository, Michele Vendruscolowork_bidkw6bwyzbcdeheliyox37juiMon, 30 May 2022 00:00:00 GMTMultiscale derivation, analysis and simulation of collective dynamics models: geometrical aspects and applications
https://scholar.archive.org/work/xmg4xzvixfbhdooukuq2wv5bki
This thesis is a contribution to the study of swarming phenomena from the point of view of mathematical kinetic theory. This multiscale approach starts from stochastic individual based (or particle) models and aims at the derivation of partial differential equation models on statistical quantities when the number of particles tends to infinity. This latter class of models is better suited for mathematical analysis in order to reveal and explain large-scale emerging phenomena observed in various biological systems such as flocks of birds or swarms of bacteria. Within this objective, a large part of this thesis is dedicated to the study of a body-attitude coordination model and, through this example, of the influence of geometry on self-organisation. The first part of the thesis deals with the rigorous derivation of partial differential equation models from particle systems with mean-field interactions. After a review of the literature, in particular on the notion of propagation of chaos, a rigorous convergence result is proved for a large class of geometrically enriched piecewise deterministic particle models towards local BGK-type equations. In addition, the method developed is applied to the design and analysis of a new particle-based algorithm for sampling. This first part also addresses the question of the efficient simulation of particle systems using recent GPU routines. The second part of the thesis is devoted to kinetic and fluid models for body-oriented particles. The kinetic model is rigorously derived as the mean-field limit of a particle system. In the spatially homogeneous case, a phase transition phenomenon is investigated which discriminates, depending on the parameters of the model, between a "disordered" dynamics and a self-organised "ordered" dynamics. The fluid (or macroscopic) model was derived as the hydrodynamic limit of the kinetic model a few years ago by Degond et al. The analytical and numerical study of this model reveal the existence of new self-organised phenomena which are confirmed a [...]Antoine Diez, Pierre Degond, Sara Merino-Aceituno, EPSRCwork_xmg4xzvixfbhdooukuq2wv5bkiWed, 25 May 2022 00:00:00 GMTAlcohol Dehydrogenases as Catalysts in Organic Synthesis
https://scholar.archive.org/work/wnsaoeo4hjh4lbsc7cvncnte7u
Alcohol dehydrogenases (ADHs) have become important catalysts for stereoselective oxidation and reduction reactions of alcohols, aldehydes and ketones. The aim of this contribution is to provide the reader with a timely update on the state-of-the-art of ADH-catalysis. Mechanistic basics are presented together with practical information about the use of ADHs. Current concepts of ADH engineering and ADH reactions are critically discussed. Finally, this contribution highlights some prominent examples and future-pointing concepts.Amanda Silva de Miranda, Cintia D. F. Milagre, Frank Hollmannwork_wnsaoeo4hjh4lbsc7cvncnte7uTue, 10 May 2022 00:00:00 GMT