The Experts below are selected from a list of 9429 Experts worldwide ranked by ideXlab platform
Wolfgang Wernsdorfer - One of the best experts on this subject based on the ideXlab platform.
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the role of the quadrupolar interaction in the tunneling dynamics of lanthanide molecular magnets
arXiv: Mesoscale and Nanoscale Physics, 2020Co-Authors: Gheorghe Taran, Edgar Bonet, Wolfgang WernsdorferAbstract:Quantum tunneling dominates the low temperature magnetization dynamics in molecular magnets and presents features that are strongly system dependent. The current discussion is focused on the terbium(III) bis(phtalocyanine) ([TbPc$_2$]$^{-1}$) complex, that should serve as a Prototypical Case for lanthanide molecular magnets. We analyze numerically the effect of non-axial interactions on the magnitude of the intrinsic tunnel splitting and show that usual suspects like the transverse ligand field and Zeeman interaction fail to explain the experimentally observed dynamics. We then propose through the nuclear quadrupolar interaction a viable mechanism that mixes, otherwise \textit{almost} degenerate hyperfine states.
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the role of the quadrupolar interaction in the tunneling dynamics of lanthanide molecular magnets
Journal of Applied Physics, 2019Co-Authors: Gheorghe Taran, Edgar Bonet, Wolfgang WernsdorferAbstract:Quantum tunneling dominates the low temperature magnetization dynamics in molecular magnets and presents features that are strongly system dependent. The current discussion is focused on the terbium(III) bis(phtalocyanine) ( [ TbPc 2 ] − 1 ) complex that should serve as a Prototypical Case for lanthanide molecular magnets. We analyze numerically the effect of non-axial interactions on the magnitude of the intrinsic tunnel splitting and show that usual suspects like the transverse ligand field and Zeeman interaction fail to explain the experimentally observed dynamics. We then propose through the nuclear quadrupolar interaction a viable mechanism that mixes, otherwise almost degenerate hyperfine states.Quantum tunneling dominates the low temperature magnetization dynamics in molecular magnets and presents features that are strongly system dependent. The current discussion is focused on the terbium(III) bis(phtalocyanine) ( [ TbPc 2 ] − 1 ) complex that should serve as a Prototypical Case for lanthanide molecular magnets. We analyze numerically the effect of non-axial interactions on the magnitude of the intrinsic tunnel splitting and show that usual suspects like the transverse ligand field and Zeeman interaction fail to explain the experimentally observed dynamics. We then propose through the nuclear quadrupolar interaction a viable mechanism that mixes, otherwise almost degenerate hyperfine states.
Isabelle Bichindaritz - One of the best experts on this subject based on the ideXlab platform.
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Prototypical Cases for retrieval reuse and knowledge maintenance in biomedical Case based reasoning
Computational Intelligence, 2009Co-Authors: Isabelle BichindaritzAbstract:Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge in biomedical fields, which often makes the content of a Case base partially obsolete. This article proposes to make use of the concept of Prototypical Case to ensure that a CBR system would keep update with current research advances in the biomedical field. Prototypical Cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of Cases, and to serve as bootstrapping a CBR system memory when real Cases are not available in sufficient quantity and/or quality. This paper emphasizes the different roles Prototypical Cases can play in CBR systems, and presents knowledge maintenance as a very important novel role for these Prototypical Cases.
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Prototypical Case mining from biomedical literature for bootstrapping a Case base
Applied Intelligence, 2008Co-Authors: Isabelle BichindaritzAbstract:This article addresses the task of mining for Cases from biomedical literature to automatically build an initial Case base for a Case-based reasoning (CBR) system. This research takes place within the Memoire project, which has for goal to provide a framework to facilitate building CBR systems in biology and medicine. By analyzing medical literature, the ProCaseMiner system mines for medical concepts such as diseases, signs and symptoms, laboratory tests, and treatment plans in relationship with one another, and connects them together in a given medical domain. It then organizes these concepts in a higher-level structure called a Case. This Case mining component provides a definite help to bootstrap the creation of a biomedical CBR system Case base, composed of both concrete Cases and Prototypical Cases. Currently, most Cases learnt correspond to Prototypical Cases, given the level of abstraction of their features. This article validates the approach by presenting a comparison between the Prototypical Cases learnt from stem-cell transplantation domain with those created by a team of experts in the domain.
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the role of Prototypical Cases in biomedical Case based reasoning
International Conference on Data Mining, 2007Co-Authors: Isabelle BichindaritzAbstract:Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise of representing contextual knowledge in a way that was not possible before with traditional knowledge representation and knowledge-based methods. A main issue in biomedical CBR has been dealing with maintenance of the Case base, and particularly in medical domains, with the rate of generation of new knowledge, which often makes the content of a Case base partially obsolete. This article proposes to make use of the concept of Prototypical Case to ensure that a CBR system would keep up-to-date with current research advances in the biomedical field. It proposes to illustrate and discuss the different roles that Prototypical Cases can serve in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of Cases, and to serve as bootstrapping a CBR system memory when real Cases are not available in sufficient quantity and/or quality. This paper presents knowledge maintenance as another role that these Prototypical Cases can play in biomedical CBR systems.
Sauro Succi - One of the best experts on this subject based on the ideXlab platform.
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lbsoft a parallel open source software for simulation of colloidal systems
Computer Physics Communications, 2020Co-Authors: Fabio Bonaccorso, Andrea Montessori, Adriano Tiribocchi, G Amati, Massimo Bernaschi, Marco Lauricella, Sauro SucciAbstract:Abstract We present LBsoft, an open-source software developed mainly to simulate the hydro-dynamics of colloidal systems based on the concurrent coupling between lattice Boltzmann methods for the fluid and discrete particle dynamics for the colloids. Such coupling has been developed before, but, to the best of our knowledge, no detailed discussion of the programming issues to be faced in order to attain efficient implementation on parallel architectures, has ever been presented to date. In this paper, we describe in detail the underlying multi-scale models, their coupling procedure, along side with a description of the relevant input variables, to facilitate third-parties usage. The code is designed to exploit parallel computing platforms, taking advantage also of the recent AVX-512 instruction set. We focus on LBsoft structure, functionality, parallel implementation, performance and availability, so as to facilitate the access to this computational tool to the research community in the field. The capabilities of LBsoft are highlighted for a number of Prototypical Case studies, such as pickering emulsions, bicontinuous systems, as well as an original study of the coarsening process in confined bijels under shear. Program summary Program Title: LBsoft CPC Library link to program files: http://dx.doi.org/10.17632/dvpfx9p342.1 Licensing provisions: BSD 3-Clause License Programming language: Fortran 95 Nature of problem: Hydro-dynamics of the colloidal multi-component systems and Pickering emulsions. Solution method: Numerical solutions to the Navier–Stokes equations by Lattice-Boltzmann (lattice-Bhatnagar–Gross–Krook, LBGK) method [1] describing the fluid dynamics within an Eulerian description. Numerical solutions to the equations of motion describing a set of discrete colloidal particles within a Lagrangian representation coupled to the LBGK solver [2]. The numerical solution of the coupling algorithm includes the back reaction effects for each force term following a multi-scale paradigm. [1] S. Succi, The Lattice Boltzmann Equation: For Fluid Dynamics and Beyond, Oxford University Press, 2001. [2] A. Ladd, R. Verberg, Lattice-Boltzmann simulations of particle-fluid suspensions, J. Stat. Phys. 104.5–6 (2001) 1191–1251.
Jorg Behler - One of the best experts on this subject based on the ideXlab platform.
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maximally resolved anharmonic oh vibrational spectrum of the water zno 101 0 interface from a high dimensional neural network potential
Journal of Chemical Physics, 2018Co-Authors: Vanessa Quaranta, Jorg Behler, Matti Hellstrom, Jolla Kullgren, Pavlin D Mitev, Kersti HermanssonAbstract:Unraveling the atomistic details of solid/liquid interfaces, e.g., by means of vibrational spectroscopy, is of vital importance in numerous applications, from electrochemistry to heterogeneous catalysis. Water-oxide interfaces represent a formidable challenge because a large variety of molecular and dissociated water species are present at the surface. Here, we present a comprehensive theoretical analysis of the anharmonic OH stretching vibrations at the water/ZnO(101¯0) interface as a Prototypical Case. Molecular dynamics simulations employing a reactive high-dimensional neural network potential based on density functional theory calculations have been used to sample the interfacial structures. In the second step, one-dimensional potential energy curves have been generated for a large number of configurations to solve the nuclear Schrodinger equation. We find that (i) the ZnO surface gives rise to OH frequency shifts up to a distance of about 4 A from the surface; (ii) the spectrum contains a number of o...
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accurate neural network description of surface phonons in reactive gas surface dynamics n2 ru 0001
Journal of Physical Chemistry Letters, 2017Co-Authors: Khosrow Shakouri, Jorg Behler, Jorg Meyer, Geertjan KroesAbstract:Ab initio molecular dynamics (AIMD) simulations enable the accurate description of reactive molecule–surface scattering especially if energy transfer involving surface phonons is important. However, presently, the computational expense of AIMD rules out its application to systems where reaction probabilities are smaller than about 1%. Here we show that this problem can be overcome by a high-dimensional neural network fit of the molecule–surface interaction potential, which also incorporates the dependence on phonons by taking into account all degrees of freedom of the surface explicitly. As shown for N2 + Ru(0001), which is a Prototypical Case for highly activated dissociative chemisorption, the method allows an accurate description of the coupling of molecular and surface atom motion and accurately accounts for vibrational properties of the employed slab model of Ru(0001). The neural network potential allows reaction probabilities as low as 10–5 to be computed, showing good agreement with experimental re...
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neural network molecular dynamics simulations of solid liquid interfaces water at low index copper surfaces
Physical Chemistry Chemical Physics, 2016Co-Authors: Suresh Kondati Natarajan, Jorg BehlerAbstract:Solid–liquid interfaces have received considerable attention in recent years due to their central role in many technologically relevant fields like electrochemistry, heterogeneous catalysis and corrosion. As the chemical processes in these examples take place primarily at the interface, understanding the structural and dynamical properties of the interfacial water molecules is of vital importance. Here, we use a first-principles quality high-dimensional neural network potential built from dispersion-corrected density functional theory data in molecular dynamics simulations to investigate water–copper interfaces as a Prototypical Case. After performing convergence tests concerning the required supercell size and water film diameter, we investigate numerous properties of the interfacial water molecules at the low-index copper (111), (100) and (110) surfaces. These include density profiles, hydrogen bond properties, lateral mean squared displacements and residence times of the water molecules at the surface. We find that in general the copper–water interaction is rather weak with the strongest interactions observed at the Cu(110) surface, followed by the Cu(100) and Cu(111) surfaces. The distribution of the water molecules in the first hydration layer exhibits a double peak structure. In all Cases, the molecules closest to the surface are predominantly allocated on top of the metal sites and are aligned nearly parallel with the oxygen pointing slightly to the surface. The more distant molecules in the first hydration layer at the Cu(111) and Cu(100) surfaces are mainly found in between the top sites, whereas at the Cu(110) surface most of these water molecules are found above the trenches of the close packed atom rows at the surface.
Giulio Biroli - One of the best experts on this subject based on the ideXlab platform.
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an analytic theory of shallow networks dynamics for hinge loss classification
arXiv: Machine Learning, 2020Co-Authors: Franco Pellegrini, Giulio BiroliAbstract:Neural networks have been shown to perform incredibly well in classification tasks over structured high-dimensional datasets. However, the learning dynamics of such networks is still poorly understood. In this paper we study in detail the training dynamics of a simple type of neural network: a single hidden layer trained to perform a classification task. We show that in a suitable mean-field limit this Case maps to a single-node learning problem with a time-dependent dataset determined self-consistently from the average nodes population. We specialize our theory to the Prototypical Case of a linearly separable dataset and a linear hinge loss, for which the dynamics can be explicitly solved. This allow us to address in a simple setting several phenomena appearing in modern networks such as slowing down of training dynamics, crossover between rich and lazy learning, and overfitting. Finally, we asses the limitations of mean-field theory by studying the Case of large but finite number of nodes and of training samples.
