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Compressing local atomic neighbourhood descriptors

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npj Computational Materials 2022年第1期8卷 1593-1605页

作者： James P.Darby James R.Kermode gábor csányi Engineering Dept University of CambridgeTrumpington StCambridgeCB21PZUK Warwick Centre for Predictive Modelling School of EngineeringUniversity of WarwickCoventryCV47ALUK

Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements S *** length of body-ordered descriptors,such as the SOAP power spectrum(3-body)and the(ACE)(multiple body-orders),scales as(NS)^(ν)whereν+1 is the body-order and N is the number of radial basis functions used in the density *** introduce two distinct approaches which can be used to overcome this scaling for the SOAP power ***,we show that the power spectrum is amenable to lossless compression with respect to both S and N,so that the descriptor length can be reduced from O(N^(2)S^(2))to O(NS).Secondly,we introduce a generalised SOAP kernel,where compression is achieved through the use of the total,element agnostic density,in combination with radial *** ideas used in the generalised kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the(AcsF).

关键词： SOAP scaling kernel

Hyperactive learning for data-driven interatomic potentials

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npj Computational Materials 2023年第1期9卷 599-612页

作者： Cas van der Oord Matthias Sachs Dávid Péter Kovács Christoph Ortner gábor csányi University of Cambridge Cambridge CB21PZUK University of Birmingham Birmingham B152TTUK University of British Columbia VancouverBC V6T 1Z2Canada

Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy *** most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training *** aid this process hyperactive learning(HAL),an accelerated active learning scheme,is presented as a method for rapid automated training database *** adds a biasing term to a physically motivated sampler(*** dynamics)driving atomic structures towards uncertainty in turn generating unseen or valuable training *** proposed HAL framework is used to develop atomic cluster expansion(ACE)interatomic potentials for the AlSi10 alloy and polyethylene glycol(PEg)polymer starting from roughly a dozen initial *** HAL generated ACE potentials are shown to be able to determine macroscopic properties,such as melting temperature and density,with close to experimental accuracy.

关键词： starting generating consuming

Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields

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npj Computational Materials 2023年第1期9卷 479-488页

作者： Lars L.Schaaf Edvin Fako Sandip De Ansgar Schäfer gábor csányi Engineering Laboratory University of CambridgeCB21PZ CambridgeUK BASF SE Carl-Bosch-Straße 3867056 LudwigshafenGermany

We introduce a training protocol for developing machine learning force fields(MLFFs),capable of accurately determining energy barriers in catalytic reaction *** protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium *** the help of active learning,the final force field obtains energy barriers within 0.05 eV of Density Functional *** to the computational speedup,not only do we reduce the cost of routine in-silico catalytic tasks,but also find an alternative path for the previously established rate-limiting step,with a 40%reduction in activation ***,we illustrate the importance of finite temperature effects and compute free energy *** transferability of the protocol is demonstrated on the experimentally relevant,yet unexplored,top-layer reduced indium oxide *** ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab-initio simulations.

关键词： surface catalytic fields

Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent

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npj Computational Materials 2023年第1期9卷 830-844页

作者： Ioan-Bogdan Magdău Daniel JArismendi-Arrieta Holly ESmith Clare Pgrey Kersti Hermansson gábor csányi Engineering Laboratory University of CambridgeTrumpington StreetCambridge CB21PZUK Department of Chemistry–Ångström Laboratory Uppsala UniversityBox 53875121 UppsalaSweden Yusuf Hamid Department of Chemistry University of CambridgeLensfield RoadCambridge CB21EWUK

Highly accurate ab initio molecular dynamics(MD)methods are the gold standard for studying molecular mechanisms in the condensed phase,however,they are too expensive to capture many key properties that converge slowly with respect to simulation length and time *** learning(ML)approaches which reach the accuracy of ab initio simulation,and which are,at the same time,sufficiently affordable hold the key to bridging this *** this work we present a robust ML potential for the EC:EMC binary solvent,a key component of liquid electrolytes in rechargeable Li-ion *** identify the necessary ingredients needed to successfully model this liquid mixture of organic *** particular,we address the challenge posed by the separation of scale between intra-and inter-molecular interactions,which is a general issue in all condensed phase molecular systems.

关键词： condensed binary Ethylene

De novo exploration and self-guided learning of potentialenergy surfaces

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npj Computational Materials 2019年第1期5卷 262-270页

作者： Noam Bernstein gábor csányi Volker L.Deringer Center for Materials Physics and Technology U.S.Naval Research LaboratoryWashingtonDC 20375USA Department of Engineering University of CambridgeCambridge CB21PZUK Department of Chemistry University of OxfordOxford OX13QRUK

Interatomic potential models based on machine learning(ML)are rapidly developing as tools for material ***,because of their flexibility,they require large fitting databases that are normally created with substantial manual selection and tuning of reference ***,we show that ML potentials can be built in a largely automated fashion,exploring and fitting potential-energy surfaces from the beginning(de novo)within one and the same *** key enabling step is the use of a configuration-averaged kernel metric that allows one to select the few most relevant and diverse structures at each *** resulting potentials are accurate and robust for the wide range of configurations that occur during structure searching,despite only requiring a relatively small number of single-point DFT calculations on small unit *** apply the method to materials with diverse chemical nature and coordination environments,marking an important step toward the more routine application of ML potentials in physics,chemistry,and materials science.

关键词： apply routine searching

Screw dislocation structure and mobility in body centered cubic Fe predicted by a gaussian Approximation Potential

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npj Computational Materials 2018年第1期4卷 90-96页

作者： Francesco Maresca Daniele Dragoni gábor csányi Nicola Marzari William A.Curtin Laboratory for Multiscale Mechanics Modeling(LAMMM) Institute of Mechanical EngineeringÉcole Polytechnique Fédérale de LausanneLausanne CH-1015Switzerland Theory and Simulation of Materials(THEOS) and National Centre for Computational Design and Discovery of Novel Materials(NCCR MARVEL)École Polytechnique Fédérale de LausanneLausanne CH-1015Switzerland Dipartimento di Scienza dei Materiali Universitàdi Milano-BicoccaVia R.Cozzi 55Milan I-20125Italy Department of Engineering University of CambridgeTrumpington StreetCambridge CB21PZUK

The plastic flow behavior of bcc transition metals up to moderate temperatures is dominated by the thermally activated glide of screw dislocations,which in turn is determined by the atomic-scale screw dislocation core structure and the associated kink-pair nucleation mechanism for *** complex plasticity phenomena requires the simulation of many atoms and interacting dislocations and *** sizes are beyond the scope of first-principles methods and thus require empirical interatomic *** for the technological important case of bcc Fe,existing empirical interatomic potentials yield spurious ***,the structure and motion of the screw dislocations in Fe are studied using a new gaussian Approximation Potential(gAP)for bcc Fe,which has been shown to reproduce the potential energy surface predicted by density-functional theory(DFT)and many associated *** Fe gAP predicts a compact,non-degenerate core structure,a single-hump Peierls potential,and glide on{110},consistent with DFT *** thermally activated motion at finite temperatures occurs by the expected kink-pair nucleation and propagation *** stress-dependent enthalpy barrier for screw motion,computed using the nudgedelastic-band method,follows closely a form predicted by standard theories with a zero-stress barrier of~1 eV,close to the experimental value of 0.84 eV,and a Peierls stress of~2 gPa consistent with DFT predictions of the Peierls potential.

关键词： structure dislocation cubic

Machine-learned interatomic potentials by active learning:amorphous and liquid hafnium dioxide

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npj Computational Materials 2020年第1期6卷 803-810页

作者： ganesh Sivaraman Anand Narayanan Krishnamoorthy Matthias Baur Christian Holm Marius Stan gábor csányi Chris Benmore Álvaro Vázquez-Mayagoitia Leadership Computing Facility Argonne National LaboratoryArgonneIL 60439USA Institute for Computational Physics Universität StuttgartAllmandring 3Stuttgart 70569Germany Helmholtz-Institute Münster:Ionics in Energy Storage(IEK-12) Forschungszentrum Jülich GmbHCorrensstrasse 46Münster 48149Germany Applied Materials Division Argonne National LaboratoryArgonneIL 60439USA Department of Engineering University of CambridgeTrumpington StreetCambridge CB21PZUK X-ray Science Division Argonne National LaboratoryArgonneIL 60439USA Computational Science Division Argonne National LaboratoryArgonneIL 60439USA

We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the gaussian Approximation Potential(gAP).Our active learning scheme consists of an unsupervised machine learning(ML)scheme coupled with a Bayesian optimization technique that evaluates the gAP *** apply this scheme to a Hafnium dioxide(HfO2)dataset generated from a“melt-quench”ab initio molecular dynamics(AIMD)*** results show that the active learning scheme,with no prior knowledge of the dataset,is able to extract a configuration that reaches the required energy fit ***,molecular dynamics(MD)simulations performed using this active learned gAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates(i.e.,1.0 K/ps)not accessible via *** melt and amorphous X-ray structural factors generated from our simulation are in good agreement with *** addition,the calculated diffusion constants are in good agreement with previous ab initio studies.

关键词： amorphous dioxide scheme

Machine-learned interatomic potentials for alloys and alloy phase diagrams

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npj Computational Materials 2021年第1期7卷 139-147页

作者： Conrad W.Rosenbrock Konstantin gubaev Alexander VShapeev Livia B.Pártay Noam Bernstein gábor csányi gus L.W.Hart Department of Physics and Astronomy Brigham Young UniversityProvoUTUSA84602 Skolkovo Institute of Science and Technology Skolkovo Innovation CenterNobel Str.3Moscow 143026Russia Department of Chemistry University of ReadingWhiteknightsReading RG66ADUK Department of Chemistry University of WarwickCoventry CV47ALUK US Naval Research Laboratory Center for Computational Materials ScienceWashingtonDC 20375USA Department of Engineering University of CambridgeTrumpington StreetCambridge CB21PZUK

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of *** compare two different *** tensor potentials(MTPs)are polynomial-like functions of interatomic distances and *** gaussian approximation potential(gAP)framework uses kernel regression,and we use the smooth overlap of atomic position(SOAP)representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor *** types of potentials give excellent accuracy for a wide range of compositions,competitive with the accuracy of cluster expansion,a benchmark for this *** both models are able to describe small deformations away from the lattice positions,SOAP-gAP excels at transferability as shown by sensible transformation paths between configurations,and MTP allows,due to its lower computational cost,the calculation of compositional phase *** the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models,we expect them to open new avenues in computational materials modeling for alloys.

关键词： alloy alloys phase

Performant implementation of the atomic cluster expansion(PACE)and application to copper and silicon

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npj Computational Materials 2021年第1期7卷 878-889页

作者： Yury Lysogorskiy Cas van der Oord Anton Bochkarev Sarath Menon Matteo Rinaldi Thomas Hammerschmidt Matous Mrovec Aidan Thompson gábor csányi Christoph Ortner Ralf Drautz ICAMS Ruhr-Universität BochumBochumGermany Engineering Laboratory University of CambridgeCambridgeUK Center for Computing Research Sandia National LaboratoriesAlbuquerqueNMUSA Department of Mathematics University of British ColumbiaVancouverBCCanada

The atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis *** we implement the atomic cluster expansion in the performant C++code PACE that is suitable for use in large-scale atomistic *** briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their *** demonstrate that the atomic cluster expansion as implemented in PACE shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate ***,general purpose parameterizations are presented for copper and silicon and evaluated in *** show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.

关键词： functions expansion cluster