IA Scholar Query: Computing Diffusion State Distance Using Green's Function and Heat Kernel on Graphs.
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 GMTHarmonic balls in Liouville quantum gravity
https://scholar.archive.org/work/saduyfraa5hxdjo7pbh766wa5m
Harmonic balls are domains which satisfy the mean-value property for harmonic functions. We establish the existence and uniqueness of harmonic balls on Liouville quantum gravity (LQG) surfaces using the obstacle problem formulation of Hele-Shaw flow. We show that LQG harmonic balls are neither Lipschitz domains nor LQG metric balls, and that the boundaries of their complementary connected components are Jordan curves. We conjecture that LQG harmonic balls are the scaling limit of internal diffusion limited aggregation (IDLA) on random planar maps. In a companion paper, we prove this in the special case of mated-CRT maps.Ahmed Bou-Rabee, Ewain Gwynnework_saduyfraa5hxdjo7pbh766wa5mFri, 11 Nov 2022 00:00:00 GMTLong time asymptotics of mixed-type Kimura diffusions
https://scholar.archive.org/work/axyqlgwtgrfo3paqweazkefmdm
This paper concerns the long-time asymptotics of diffusions with degenerate coefficients at the domain's boundary. Degenerate diffusion operators with mixed linear and quadratic degeneracies find applications in the analysis of asymmetric transport at edges separating topological insulators. In one space dimension, we characterize all possible invariant measures for such a class of operators and in all cases show exponential convergence of the Green's kernel to such invariant measures. We generalize the results to a class of two-dimensional operators including those used in the analysis of topological insulators. Several numerical simulations illustrate our theoretical findings.Guillaume Bal, Binglu Chen, Zhongjian Wangwork_axyqlgwtgrfo3paqweazkefmdmWed, 02 Nov 2022 00:00:00 GMTLogarithmic corrections to the Alexander-Orbach conjecture for the four-dimensional uniform spanning tree
https://scholar.archive.org/work/mntisfmnrne6fkwbclxvlkpwqu
We compute the precise logarithmic corrections to Alexander-Orbach behaviour for various quantities describing the geometric and spectral properties of the four-dimensional uniform spanning tree. In particular, we prove that the volume of an intrinsic n-ball in the tree is n^2 (log n)^-1/3+o(1), that the typical intrinsic displacement of an n-step random walk is n^1/3 (log n)^1/9-o(1), and that the n-step return probability of the walk decays as n^-2/3(log n)^1/9-o(1).Noah Halberstam, Tom Hutchcroftwork_mntisfmnrne6fkwbclxvlkpwquWed, 02 Nov 2022 00:00:00 GMTStructural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library
https://scholar.archive.org/work/knyilha67zcenadcjzeolnevu4
A non-trivial interplay rules the relationship between the structure and the chemophysical properties of a nanoparticle. In this context, characterization experiments, molecular dynamics simulations and electronic structure calculations may allow...Robert M. Jones, Kevin Rossi, Claudio Zeni, Igor Vasiljevic, Mirko Vanzan, Alejandro Santana Bonilla, Francesca Balettowork_knyilha67zcenadcjzeolnevu4Mon, 24 Oct 2022 00:00:00 GMTIntegral Equation Methods for the Morse-Ingard Equations
https://scholar.archive.org/work/iqrp6pazujhzrmjakikfnlbbyu
We present two (a decoupled and a coupled) integral-equation-based methods for the Morse-Ingard equations subject to Neumann boundary conditions on the exterior domain. Both methods are based on second-kind integral equation (SKIE) formulations. The coupled method is well-conditioned and can achieve high accuracy. The decoupled method has lower computational cost and more flexibility in dealing with the boundary layer; however, it is prone to the ill-conditioning of the decoupling transform and cannot achieve as high accuracy as the coupled method. We show numerical examples using a Nystr\"om method based on quadrature-by-expansion (QBX) with fast-multipole acceleration. We demonstrate the accuracy and efficiency of the solvers in both two and three dimensions with complex geometry.Xiaoyu Wei, Andreas Klöckner, Robert C. Kirbywork_iqrp6pazujhzrmjakikfnlbbyuSat, 22 Oct 2022 00:00:00 GMTFrom Varadhan's Limit to Eigenmaps: A Guide to the Geometric Analysis behind Manifold Learning
https://scholar.archive.org/work/4bvs7elnfjcorlqsd2bi5h6itu
We present an overview of the history of the heat kernel and eigenfunctions on Riemannian manifolds and how the theory has lead to modern methods of analyzing high dimensional data via eigenmaps and other spectral embeddings. We begin with Varadhan's Theorem relating the heat kernel to the distance function on a Riemannian manifold. We then review various theorems which bound the heat kernel on classes of Riemannian manifolds. Next we turn to eigenfunctions, the Sturm-Liouville Decomposition of the heat kernel using eigenfunctions, and various theorems which bound eigenfunctions on classes of Riemannian manifolds. We review various notions of convergence of Riemannian manifolds and which classes of Riemannian manifolds are compact with respect to which notions of convergence. We then present B\'erard-Besson-Gallot's heat kernel embeddings of Riemannian manifolds and the truncation of those embeddings. Finally we turn to Applications of Spectral embeddings to the Dimension Reduction of data sets lying in high dimensional spaces reviewing, in particular, the work of Belkin-Niyogi and Coifman-Lafon. We also review the Spectral Theory of Graphs and the work of Dodziuk and Chung and others. We close with recent theorems of Portegies and of the first author controlling truncated spectral embeddings uniformly on key classes of Riemannian manifolds. Throughout we provide many explicitly computed examples and graphics and attempt to provide as complete a set of references as possible. We hope that this article is accessible to both pure and applied mathematicians working in Geometric Analysis and their doctoral students.Chen-Yun Lin, Christina Sormaniwork_4bvs7elnfjcorlqsd2bi5h6ituWed, 19 Oct 2022 00:00:00 GMTRecipe for a General, Powerful, Scalable Graph Transformer
https://scholar.archive.org/work/fmywxspnyjbb7mnmqqhr5ealym
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being local, global or relative. The prior GTs are constrained to small graphs with a few hundred nodes, here we propose the first architecture with a complexity linear in the number of nodes and edges O(N+E) by decoupling the local real-edge aggregation from the fully-connected Transformer. We argue that this decoupling does not negatively affect the expressivity, with our architecture being a universal function approximator on graphs. Our GPS recipe consists of choosing 3 main ingredients: (i) positional/structural encoding, (ii) local message-passing mechanism, and (iii) global attention mechanism. We provide a modular framework GraphGPS that supports multiple types of encodings and that provides efficiency and scalability both in small and large graphs. We test our architecture on 16 benchmarks and show highly competitive results in all of them, show-casing the empirical benefits gained by the modularity and the combination of different strategies.Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beainiwork_fmywxspnyjbb7mnmqqhr5ealymWed, 12 Oct 2022 00:00:00 GMTMulti-scale Incoherent Electronic Transport Properties in Non-ideal CVD Graphene Devices
https://scholar.archive.org/work/62bydhfgsfaovkf6y6lnbjlkve
In this research work, roll-to-roll chemical vapor deposited graphene device electronic transport properties are benchmarked to elucidate and comprehend mobility degradation in the real-world commercial application of graphene devices. Multifarious device design morphology in the graphene and two-dimensional material with diverse background materials compositions and processing recipes incorporate various scattering sources in the devices. To understand the nature of mobility degradation in roll-to-roll chemical vapor deposited graphene production devices, we employed multi-scale, multi-physics bottom-up, non-equilibrium Green's function-based quantum transport formalism. In this framework, we numerically incorporate various scattering mechanisms to deduce the measurand mobility at the last stage of computation to observe various scattering potential impacts on the production device performance. We have analyzed the variation in transmission, electronic charge density, electrostatic Poisson potential, energy-resolved flux density, and current-voltage characteristics and inferred the Drude mobility with different scattering potentials in various graphene devices. These scattering mechanisms treat scattering potentials as the first-order phonon Dyson self-energy term in the third loop of the two-looped self-consistent Poisson-Non-equilibrium Green's function iteration. Furthermore, multiple scattering scenarios implemented through a generalized contact self-energy scattering calculation ascribe the effect of contact scattering to the graphene device in quasi-ballistic transport limit. In this scheme, the effect of all the physical scattering mechanisms is lumped into one energy uncertainty or scattering rate parameter to include in the device's contact self-energy interaction term bound by the upper limit of Heisenberg uncertainty for the interacting quantum charged particles.Bhupesh Bishnoiwork_62bydhfgsfaovkf6y6lnbjlkveTue, 11 Oct 2022 00:00:00 GMTEdge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
https://scholar.archive.org/work/anbpbg2wsbfbtavecexe2www6y
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable recent successful MTS methods are built with graph neural networks (GNNs) due to their essential capacity for relational modeling. However, previous work often used a static graph structure of time-series variables for modeling MTS failing to capture their ever-changing correlations over time. To this end, a fully-connected supra-graph connecting any two variables at any two timestamps is adaptively learned to capture the high-resolution variable dependencies via an efficient graph convolutional network. Specifically, we construct the Edge-Varying Fourier Graph Networks (EV-FGN) equipped with Fourier Graph Shift Operator (FGSO) which efficiently performs graph convolution in the frequency domain. As a result, a high-efficiency scale-free parameter learning scheme is derived for MTS analysis and forecasting according to the convolution theorem. Extensive experiments show that EV-FGN outperforms state-of-the-art methods on seven real-world MTS datasets.Kun Yi and Qi Zhang and Liang Hu and Hui He and Ning An and LongBing Cao and ZhenDong Niuwork_anbpbg2wsbfbtavecexe2www6ySun, 09 Oct 2022 00:00:00 GMTFluid Flow Processes at Mid-Ocean Ridge Hydrothermal Systems
https://scholar.archive.org/work/7z34zwfggnbu7kub77ecdmu6ma
The subseafloor structure and temporal variability of mid-ocean ridge hydrothermal systems are examined from a largely theoretical standpoint. The nature of tidal signals is considered in detail and there is a discussion of the mechanisms by which the tidal modulations observed at seafloor hydrothermal systems might be produced. A review of the known examples of tidal modulation at hydrothermal systems is presented, and a new procedure for the analysis of these tidally modulated time-series is proposed. Where possible, this new procedure is applied to datasets previously obtained at the seafloor and it is recommended for use in future analyses. It is shown that the nonlinear thermodynamic properties of pure water are sufficient to impose a structure consistent with the known constraints on subseafloor convection cells. In particular, it is demonstrated that the properties of water limit seafloor vent temperatures to ~400°C, even when the energy source driving the convection cell is much hotter. A scaling analysis is presented to reveal how the lengthscales and timescales associated with a subseafloor convection cell depend on the bulk crustal permeability. The equations of poroelasticity are reviewed to demonstrate how the nonlinear thermodynamic properties of water influence the response of a hydrothermal system to tidal loading at the seafloor. A selection of simple analytical solutions reveals the phase relationship of the effluent temperature and effluent velocity at the seafloor to the ocean tide. A numerical simulation illustrates the effect of tidal loading on a two-dimensional subseafloor convection cell incorporating the nonlinear properties of water.Timothy Edmund Jupp, Apollo-University Of Cambridge Repository, Adam Schultzwork_7z34zwfggnbu7kub77ecdmu6maThu, 06 Oct 2022 00:00:00 GMTDiffWire: Inductive Graph Rewiring via the Lovász Bound
https://scholar.archive.org/work/hurjkdnedvhidmzvnn72idvwte
Graph Neural Networks (GNNs) have been shown to achieve competitive results to tackle graph-related tasks, such as node and graph classification, link prediction and node and graph clustering in a variety of domains. Most GNNs use a message passing framework and hence are called MPNNs. Despite their promising results, MPNNs have been reported to suffer from over-smoothing, over-squashing and under-reaching. Graph rewiring and graph pooling have been proposed in the literature as solutions to address these limitations. However, most state-of-the-art graph rewiring methods fail to preserve the global topology of the graph, are neither differentiable nor inductive, and require the tuning of hyper-parameters. In this paper, we propose DiffWire, a novel framework for graph rewiring in MPNNs that is principled, fully differentiable and parameter-free by leveraging the Lov\'asz bound. Our approach provides a unified theory for graph rewiring by proposing two new, complementary layers in MPNNs: CT-Layer, a layer that learns the commute times and uses them as a relevance function for edge re-weighting; and GAP-Layer, a layer to optimize the spectral gap, depending on the nature of the network and the task at hand. We empirically validate the value of each of these layers separately with benchmark datasets for graph classification. DiffWire brings together the learnability of commute times to related definitions of curvature, opening the door to creating more expressive MPNNs.Adrian Arnaiz-Rodriguez, Ahmed Begga, Francisco Escolano, Nuria Oliverwork_hurjkdnedvhidmzvnn72idvwteTue, 27 Sep 2022 00:00:00 GMTModelling solid/fluid interactions in hydrodynamic flows: a hybrid multiscale approach
https://scholar.archive.org/work/uuzf6ksrfvberijkhxuk55m2ne
With the advent of high performance computing (HPC), we can simulate nature at time and length scales that we could only dream of a few decades ago. Through the development of theory and numerical methods in the last fifty years, we have at our disposal a plethora of mathematical and computational tools to make powerful predictions about the world which surrounds us. From quantum methods like Density Functional Theory (DFT); going through atomistic methods such as Molecular Dynamics (MD) and Monte Carlo (MC), right up to more traditional macroscopic techniques based on Partial Differential Equations (PDEs) discretization like the Finite Element Method (FEM) or Finite Volume Method (FVM), which are respectively, the foundation of computational Structural Analysis and Computational Fluid Dynamics (CFD). Many modern scientific computing challenges in physics stem from combining appropriately two or more of these methods, in order to tackle problems that could not be solved otherwise using just one of them alone. This is known as multi-scale modeling, which aims to achieve a trade-off between computational cost and accuracy by combining two or more physical models at different scales. In this work, a multi-scale domain decomposition technique based on coupling MD and CFD methods, has been developed to make affordable the study of slip and friction, with atomistic detail, at length scales otherwise impossible by fully atomistic methods alone. A software framework has been developed to facilitate the execution of this particular kind of simulations on HPC clusters. This have been possible by employing the in-house developed CPL_LIBRARY software library, which provides key functionality to implement coupling through domain decomposition.Eduardo Ramos Fernandez, Daniele Dini, David Heyes, BP-ICAM, Engineering And Physical Sciences Research Council (EPSRC)work_uuzf6ksrfvberijkhxuk55m2neFri, 23 Sep 2022 00:00:00 GMTInterface dynamics in the two-dimensional quantum Ising model
https://scholar.archive.org/work/st6shht7cfanvfiggp3ty23k4m
In a recent paper [Phys. Rev. Lett. 129, 120601] we have shown that the dynamics of interfaces, in the symmetry-broken phase of the two-dimensional ferromagnetic quantum Ising model, displays a robust form of ergodicity breaking. In this paper, we elaborate more on the issue. First, we discuss two classes of initial states on the square lattice, the dynamics of which is driven by complementary terms in the effective Hamiltonian and may be solved exactly: (a) strips of consecutive neighbouring spins aligned in the opposite direction of the surrounding spins, and (b) a large class of initial states, characterized by the presence of a well-defined "smooth" interface separating two infinitely extended regions with oppositely aligned spins. The evolution of the latter states can be mapped onto that of an effective one-dimensional fermionic chain, which is integrable in the infinite-coupling limit. In this case, deep connections with noteworthy results in mathematics emerge, as well as with similar problems in classical statistical physics. We present a detailed analysis of the evolution of these interfaces both on the lattice and in a suitable continuum limit, including the interface fluctuations and the dynamics of entanglement entropy. Second, we provide analytical and numerical evidence supporting the conclusion that the observed non-ergodicity – arising from Stark localization of the effective fermionic excitations – persists away from the infinite-Ising-coupling limit, and we highlight the presence of a timescale T∼ e^c Lln L for the decay of a region of large linear size L. The implications of our work for the classic problem of the decay of a false vacuum are also discussed.Federico Balducci, Andrea Gambassi, Alessio Lerose, Antonello Scardicchio, Carlo Vanoniwork_st6shht7cfanvfiggp3ty23k4mMon, 19 Sep 2022 00:00:00 GMTScaling limits of loop-erased Markov chains on resistance spaces via a partial loop-erasing procedure
https://scholar.archive.org/work/ooinokttyjf53hc4idq4aktgmi
We introduce partial loop-erasing operators. We show that by applying a refinement sequence of partial loop-erasing operators to a finite Markov chain, we get a process equivalent to the chronological loop-erased Markov chain. As an application, we construct loop-erased random paths on bounded domains of resistance spaces as the weak limit of the loop erasure of the Markov chains on a sequence of finite sets approximating the space, and the limit is independent of the approximating sequences. The random paths we constructed are simple paths almost surely, and they can be viewed as the loop-erasure of the paths of the diffusion process. Finally, we show that the scaling limit of the loop-erased random walks on the Sierpi\'nski carpet graphs exists, and is equivalent to the loop-erased random paths on the Sierpi\'nksi carpet.Shiping Caowork_ooinokttyjf53hc4idq4aktgmiThu, 15 Sep 2022 00:00:00 GMTQuantum Field Theory Anomalies in Condensed Matter Physics
https://scholar.archive.org/work/kguxl4rcqzfsllo2shl4isq2em
We give a pedagogical introduction to quantum anomalies, how they are calculated using various methods, and why they are important in condensed matter theory. We discuss axial, chiral, and gravitational anomalies as well as global anomalies. We illustrate the theory with examples such as quantum Hall liquids, Fermi liquids, Weyl semi-metals, topological insulators and topological superconductors. The required background is basic knowledge of quantum field theory, including fermions and gauge fields, and some familiarity with path integral and functional methods. Some knowledge of topological phases of matter is helpful, but not necessary.Rodrigo Arouca, Andrea Cappelli, Hans Hanssonwork_kguxl4rcqzfsllo2shl4isq2emThu, 08 Sep 2022 00:00:00 GMTNonlinear eigenvalue methods for linear pointwise stability of nonlinear waves
https://scholar.archive.org/work/acv5fnhpdvdzjget2i64h3af2a
We propose an iterative method to find pointwise growth exponential growth rates in linear problems posed on essentially one-dimensional domains. Such pointwise growth rates capture pointwise stability and instability in extended systems and arise as spectral values of a family of matrices that depends analytically on a spectral parameter, obtained via a scattering-type problem. Different from methods in the literature that rely on computing determinants of this nonlinear matrix pencil, we propose and analyze an inverse power method that allows one to locate robustly the closest spectral value to a given reference point in the complex plane. The method finds branch points, eigenvalues, and resonance poles without a priori knowledge.Arnd Scheelwork_acv5fnhpdvdzjget2i64h3af2aMon, 29 Aug 2022 00:00:00 GMTThe Variational Quantum Eigensolver: a review of methods and best practices
https://scholar.archive.org/work/zlqmzjpmjngy3bc4pboozn46yq
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian, a problem that is central to quantum chemistry and condensed matter physics. Conventional computing methods are constrained in their accuracy due to the computational limits. The VQE may be used to model complex wavefunctions in polynomial time, making it one of the most promising near-term applications for quantum computing. Finding a path to navigate the relevant literature has rapidly become an overwhelming task, with many methods promising to improve different parts of the algorithm. Despite strong theoretical underpinnings suggesting excellent scaling of individual VQE components, studies have pointed out that their various pre-factors could be too large to reach a quantum computing advantage over conventional methods. This review aims to provide an overview of the progress that has been made on the different parts of the algorithm. All the different components of the algorithm are reviewed in detail including representation of Hamiltonians and wavefunctions on a quantum computer, the optimization process, the post-processing mitigation of errors, and best practices are suggested. We identify four main areas of future research:(1) optimal measurement schemes for reduction of circuit repetitions; (2) large scale parallelization across many quantum computers;(3) ways to overcome the potential appearance of vanishing gradients in the optimization process, and how the number of iterations required for the optimization scales with system size; (4) the extent to which VQE suffers for quantum noise, and whether this noise can be mitigated. The answers to these open research questions will determine the routes for the VQE to achieve quantum advantage as the quantum computing hardware scales up and as the noise levels are reduced.Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H. Booth, Jonathan Tennysonwork_zlqmzjpmjngy3bc4pboozn46yqThu, 25 Aug 2022 00:00:00 GMTDeep convolutional surrogates and degrees of freedom in thermal design
https://scholar.archive.org/work/2qhzmmdmxvainkjsmyhfwom6je
We present surrogate models for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bezier curves. Thermal design process includes iterative high fidelity simulation which is complex, computationally expensive, and time-consuming. With the advancement in machine learning algorithms as well as Graphics Processing Units (GPUs), we can utilize the parallel processing architecture of GPUs rather than solely relying on CPUs to accelerate the thermo-fluid simulation. In this study, Convolutional Neural Networks (CNNs) are used to predict results of Computational Fluid Dynamics (CFD) directly from topologies saved as images. The case with a single fin as well as multiple morphable fins are studied. A comparison of Xception network and regular CNN is presented for the case with a single fin design. Results show that high accuracy in prediction is observed for single fin design particularly using Xception network. Increasing design freedom to multiple fins increases the error in prediction. This error, however, remains within three percent for pressure drop and heat transfer estimation which is valuable for design purpose.Hadi Keramati, Feridun Hamdullahpurwork_2qhzmmdmxvainkjsmyhfwom6jeTue, 16 Aug 2022 00:00:00 GMTOptimising Stable Radicals for the Electrochemical Generation of Reactive Intermediates
https://scholar.archive.org/work/eawknghuzvcrnn6sgpxgsoj7w4
This thesis concentrates on the electrochemical activation of stable-radical adducts to generate reactive intermediates for small molecule and polymer chemistry. The majority of this work concerns the computational modelling and design of such compounds using high-level, ab inito quantum chemistry methods. The main findings are as follows. It is first shown that adducts based on highly-stable Blatter and Kuhn-type radicals undergo mesolytic cleavage upon one-electron oxidation, generating reactive carbocations or carbon-centred radicals. Substituent effects are employed to optimise this chemistry, either to reduce the oxidation potential of the adduct to favour the production of radicals, or by altering the bond-dissociation free energy of mesolytic cleavage to control the rate of fragmentation. Computational chemistry is then used to explore the scope for stable-radical adducts as electrochemically activated alkylating agents. SN2-type methylations of pyridine are studied over a broad range of nitroxide, triazinyl, and verdazyl-based adducts (X-Me). Here, high oxidation potentials are found to render low SN2 barriers to methylation and thus more reactive agents, highlighting the suitability of commercially available, (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO), in this role. Modelling is also applied to study the triboelectrification of polymeric insulators. Here, material-specific charging properties and dissipation rates are found to be connected to the stability of anionic polymer fragments to oxidation, and cationic fragments to reduction. Computational methods are then used to study the low-frequency (Terahertz) vibrations in molecular crystals. A method benchmark is presented - identifying parameters that reliably produce accurate simulated spectra - along with several new analytical tools built for the assessment of spectral data.Fergus Rogers, University, The Australian Nationalwork_eawknghuzvcrnn6sgpxgsoj7w4Sat, 13 Aug 2022 00:00:00 GMT