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Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe

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npj Computational Materials 2022年第1期8卷 107-112页

作者： Ivan Novikov Blazej Grabowski Fritz Körmann alexander shapeev Skolkovo Institute of Science and Technology Skolkovo Innovation CenterNobel St.3Moscow143026Russia Institute for Materials Science University of StuttgartPfaffenwaldring 5570569StuttgartGermany Computational Materials Design Max-Planck-Institut für Eisenforschung GmbHD-40237DüsseldorfGermany Materials Science and Engineering Delft University of Technology2628 CDDelftThe Netherlands

We present the magnetic Moment Tensor Potentials(mMTPs),a class of machine-learning interatomic potentials,accurately reproducing both vibrational and magnetic degrees of freedom as provided,e.g.,from first-principles *** accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin *** performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron,with applications to phonon calculations for different magnetic states,and molecular-dynamics simulations with fluctuating magnetic moments.

关键词： magnetic fluctuating states

Machine learning-driven synthesis of TiZrNbHfTaC_(5) high-entropy carbide

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npj Computational Materials 2023年第1期9卷 2293-2303页

作者： alexander Ya.Pak Vadim Sotskov Arina A.Gumovskaya Yuliya Z.Vassilyeva Zhanar S.Bolatova Yulia A.Kvashnina Gennady Ya.Mamontov alexander V.shapeev alexander G.Kvashnin National Research Tomsk Polytechnic University 30 Lenin AvenueTomsk 634050Russia Skolkovo Institute of Science and Technology Skolkovo Innovation CenterBolshoi Blv.30Building 1Moscow 121205Russia Pirogov Russian National Research Medical University 1 Ostrovityanova St.Moscow 117997Russia

Synthesis of high-entropy carbides(HEC)requires high temperatures that can be provided by electric arc plasma ***,the formation temperature of a single-phase sample remains ***,under some temperatures multi-phase structures can *** this work,we developed an approach for a controllable synthesis of HEC TiZrNbHfTaC_(5) based on theoretical and experimental *** used Canonical Monte Carlo(CMC)simulations with the machine learning interatomic potentials to determine the temperature conditions for the formation of single-phase and multi-phase *** full agreement with the theory,the single-phase sample,produced with electric arc discharge,was observed at 2000 *** 1200 K,the sample decomposed into(Ti-Nb-Ta)C,and a mixture of(Zr-Hf-Ta)C,(Zr-Nb-Hf)C,(Zr-Nb)C,and(Zr-Ta)*** results demonstrate the conditions for the formation of HEC and we anticipate that our approach can pave the way towards targeted synthesis of multicomponent materials.

关键词： carbide materials

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

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npj Computational Materials 2021年第1期7卷 694-703页

作者： Evgenii Tsymbalov Zhe Shi Ming Dao Subra Suresh Ju Li alexander shapeev Skolkovo Institute of Science and Technology MoscowRussia Department of Materials Science and Engineering and Department of Nuclear Science and Engineering Massachusetts Institute of TechnologyCambridgeMAUSA Nanyang Technological University SingaporeRepublic of Singapore

The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor *** the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond,employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit,through the design of an optimal straining *** simulations,however,call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical *** by this challenge,we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in *** calculations enable us to identify ways in which the physical properties can be altered through“deep”elastic strain engineering up to a large fraction of the ideal *** capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band *** training a surrogate model with ab initio computational data,our method can identify the most efficient strain energy pathway to realize physical property *** power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron *** illustrate the applications of the method with specific results for diamonds,although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials。

关键词： strain deformation electronic

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

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npj Computational Materials 2019年第1期5卷 670-676页

作者： Tatiana Kostiuchenko Fritz Körmann Jörg Neugebauer alexander shapeev Skolkovo Institute of Science and Technology Skolkovo Innovation CenterNobel St.3Moscow 143026Russia Computational Materials Design Max-Planck-Institut für Eisenforschung GmbH40237 DüsseldorfGermany Materials Science and Engineering Delft University of Technology2628 CD DelftThe Netherlands

Recently,high-entropy alloys(HEAs)have attracted wide attention due to their extraordinary materials properties.A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional *** initio calculations have emerged as a powerful approach that complements ***,for multicomponent alloys existing approaches suffer from the chemical complexity *** this work we propose a method for studying HEAs *** approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo *** high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW *** approach is employed to study phase stability,phase transitions,and chemical short-range *** importance of including local relaxation effects is revealed:they significantly stabilize single-phase formation of bcc NbMoTaW down to room ***,a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.

关键词： alloy relaxation entropy

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

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npj Computational Materials 2020年第1期6卷 46-57页

作者： Qi Wang Jun Ding Longfei Zhang Evgeny Podryabinkin alexander shapeev Evan Ma Department of Materials Science and Engineering Johns Hopkins UniversityBaltimoreMD 21218USA Center for Advancing Materials Performance from the Nanoscale(CAMP-Nano) State Key Laboratory for Mechanical Behavior of MaterialsXi’an Jiaotong UniversityXi’an 710049China Department of Computer Science Johns Hopkins UniversityBaltimoreMD 21218USA Center for Energy Science and Technology Skolkovo Institute of Science and Technology3 Nobel StreetMoscow 143026Russia

The elementary excitations in metallic glasses(MGs),i.e.,β processes that involve hopping between nearby sub-basins,underlie many unusual properties of the amorphous alloys.A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions,however,has remained a daunting ***,employing well-designed site environment descriptors and machine learning(ML),notable progress has been made in predicting the propensity for stress-activated β processes(i.e.,shear transformations)from the static structure.

关键词： structure glasses metallic

Ab initio vibrational free energies including anharmonicity for multicomponent alloys

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npj Computational Materials 2019年第1期5卷 454-459页

作者： Blazej Grabowski Yuji Ikeda Prashanth Srinivasan Fritz Körmann Christoph Freysoldt Andrew Ian Duff alexander shapeev Jörg Neugebauer Institute of Materials Science University of StuttgartPfaffenwaldring 5570569 StuttgartGermany Department for Computational Materials Design Max-Planck-Institut für Eisenforschung GmbHMax-Planck-Str.140237 DüsseldorfGermany Department of Materials Science and Engineering Delft University of TechnologyMekelweg 22628 CD DelftNetherlands Scientific Computing Department STFC Daresbury LaboratoryHartree CentreWarringtonUK Skolkovo Institute of Science and Technology Skolkovo Innovation CenterNobel St.3Moscow 143026Russia

The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific *** vast compositional parameter space makes these alloys a unique area of exploration by means of computational ***,as of now a method to compute efficiently,yet with high accuracy the thermodynamic properties of such alloys has been *** of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent *** this work,a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning *** demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy.

关键词： alloys alloy thermodynamic

Machine-learned multi-system surrogate models for materials prediction

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npj Computational Materials 2019年第1期5卷 702-707页

作者： Chandramouli Nyshadham Matthias Rupp Brayden Bekker alexander V.shapeev Tim Mueller Conrad W.Rosenbrock Gábor Csányi David W.Wingate Gus L.W.Hart Department of Physics and Astronomy Brigham Young UniversityProvoUT 84602USA Fritz Haber Institute of the Max Planck Society Faradayweg 4–614195 BerlinGermany Skolkovo Institute of Science and Technology Skolkovo Innovation CenterBuilding 3Moscow 143026Russia Department of Materials Science and Engineering Johns Hopkins UniversityBaltimoreMD 21218USA Engineering Laboratory University of CambridgeTrumpington StreetCambridge CB21PZUK Computer Science Department Brigham Young UniversityProvoUT 84602USA Citrine Informatics 702 Marshall StreetRedwood CityCA 94063USA

Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational *** demonstrate surrogate models that simultaneously interpolate energies of different materials on a dataset of 10 binary alloys(AgCu,AlFe,AlMg,AlNi,AlTi,CoNi,CuFe,CuNi,FeV,and NbNi)with 10 different species and all possible fcc,bcc,and hcp structures up to eight atoms in the unit cell,15,950 structures in *** find that the deviation of prediction errors when increasing the number of simultaneously modeled alloys is<1 meV/*** state-of-the-art materials representations and learning algorithms were found to qualitatively agree on the prediction errors of formation enthalpy with relative errors of<2.5% for all systems.

关键词： alloys prediction CoNi