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A high-throughput framework for determining adsorption energies on solid surfaces

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npj Computational Materials 2017年第1期3卷 357-360页

作者： Joseph H.Montoya kristin A.persson Lawrence Berkeley National Laboratory 1 Cyclotron RoadBerkeleyCA 94720USA Department of Materials Science and Engineering University of CaliforniaBerkeleyCA 94720USA

In this work,we present a high-throughput workflow for calculation of adsorption energies on solid surfaces using density functional *** open-source computational tools from the Materials Project infrastructure,we automate the procedure of constructing symmetrically distinct adsorbate configurations for arbitrary *** algorithms are further used to construct and run workflows in a standard,automated way such that user intervention in the simulation procedure is *** validate our approach,we compare results from our workflow to previous experimental and theoretical benchmarks from the CE27 database of chemisorption energies on solid *** benchmarks also illustrate how the task of performing and managing over 200 individual density functional theory calculations may be reduced to a single submission procedure and subsequent *** enabling more efficient high-throughput computations of adsorption energies,these tools will accelerate theory-guided discovery of advanced materials for applications in catalysis and surface science.

关键词： theory. adsorption determining

A charge-density-based general cation insertion algorithm for generating new Li-ion cathode materials

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npj Computational Materials 2020年第1期6卷 336-342页

作者： Jimmy-Xuan Shen Matthew Horton kristin A.persson Department of Materials Science and Engineering University of CaliforniaBerkeleyBerkeleyCA 94720USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeley 94720USA

Future lithium(Li)energy storage technologies,in particular solid-state configurations with a Li metal anode,opens up the possibility of using cathode materials that do not necessarily contain Li in its as-made *** accelerate the discovery and design of such materials,we develop a general,chemically,and structurally agnostic methodology for identifying the optimal Li sites in any crystalline *** a given crystal structure,we attempt multiple Li insertions at symmetrically in-equivalent positions by analyzing the electronic charge density obtained from first-principles density functional *** this report,we demonstrate the effectiveness of this procedure in successfully identifying the positions of the Li ion in well-known cathode materials using only the empty host(charged)material as ***,applying the algorithm to over 2000 candidate cathode empty host materials we obtain statistics of Li site preferences to guide future developments of novel Li-ion cathode materials,particularly for solid-state applications.

关键词： charge insertion algorithm

High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory

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npj Computational Materials 2019年第1期5卷 588-598页

作者： Matthew Kristofer Horton Joseph Harold Montoya Miao Liu kristin Aslaug persson Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Institute of Physics Chinese Academy of SciencesBeijingChina Department of Materials Science University of California BerkeleyBerkeleyCA 94720USA

We present a robust,automatic high-throughput workflow for the calculation of magnetic ground state of solid-state inorganic crystals,whether ferromagnetic,antiferromagnetic or ferrimagnetic,and their associated magnetic moments within the framework of collinear spin-polarized Density Functional *** is done through a computationally efficient scheme whereby plausible magnetic orderings are first enumerated and prioritized based on symmetry,and then relaxed and their energies determined through conventional DFT+U *** automated workflow is formalized using the atomate code for reliable,systematic use at a scale appropriate for thousands of materials and is fully *** performance of the workflow is evaluated against a benchmark of 64 experimentally known mostly ionic magnetic materials of non-trivial magnetic order and by the calculation of over 500 distinct magnetic orderings.A non-ferromagnetic ground state is correctly predicted in 95% of the benchmark materials,with the experimentally determined ground state ordering found exactly in over 60% of *** of the ground state magnetic order at scale opens up the possibility of high-throughput screening studies based on magnetic properties,thereby accelerating discovery and understanding of new functional materials.

关键词： state ground ferromagnetic

Evaluation of thermodynamic equations of state across chemistry and structure in the materials project

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npj Computational Materials 2018年第1期4卷 323-329页

作者： Katherine Latimer Shyam Dwaraknath Kiran Mathew Donald Winston kristin A.persson Department of Physics University of California BerkeleyBerkeleyCA 94720USA Energy Storage and Distributed Resources Division Lawrence Berkeley National LabBerkeleyCA 94720USA Department of Materials Science University of California BerkeleyBerkeleyCA 94720USA

Thermodynamic equations of state(EOS)for crystalline solids describe material behaviors under changes in pressure,volume,entropy and temperature,making them fundamental to scientific research in a wide range of fields including geophysics,energy storage and development of novel *** over a century of theoretical development and experimental testing of energy–volume(E–V)EOS for solids,there is still a lack of consensus with regard to which equation is indeed optimal,as well as to what metric is most appropriate for making this *** this study,several metrics were used to evaluate quality of fit for 8 different EOS across 87 elements and over 100 compounds which appear in the *** findings do not indicate a clear“best”EOS,but we identify three which consistently perform well relative to the rest of the ***,we find that for the aggregate data set,the RMSrD is not strongly correlated with the nature of the compound,e.g.,whether it is a metal,insulator,or semiconductor,nor the bulk modulus for any of the EOS,indicating that a single equation can be used across a broad range of classes of materials.

关键词： materials. solids state

Topological graph-based analysis of solid-state ion migration

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npj Computational Materials 2023年第1期9卷 1337-1341页

作者： Jimmy-Xuan Shen Haoming Howard Li Ann Rutt Matthew K.Horton kristin A.persson Materials Science Division Lawrence Livermore National LaboratoryLivermoreCAUSA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyUSA Department of Material Science and Engineering University of CaliforniaBerkeleyCAUSA Molecular Foundry Lawrence Berkeley National LaboratoryBerkeleyUSA

To accelerate the development of ion conducting materials,we present a general graph-theoretic analysis framework for ion migration in any crystalline *** nodes of the graph represent metastable sites of the migrating ion and the edges represent discrete migration events between adjacent *** from a collection of possible metastable migration sites,the framework assigns a weight to the edges by calculating the individual migration energy barriers between those *** pathways in the periodic simulation cell corresponding to macroscopic ion migration are identified by searching for the lowest-cost cycle in the periodic migration *** exemplify the utility of the framework,we present the automatic analyses of Li migration in different polymorphs of VO(PO_(4)),with the resulting identification of two distinct crystal structures with simple migration pathways demonstrating overall<300 meV migration barriers.

关键词： analysis migration metastable

An improved symmetry-based approach to reciprocal space path selection in band structure calculations

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npj Computational Materials 2020年第1期6卷 731-736页

作者： Jason M.Munro Katherine Latimer Matthew K.Horton Shyam Dwaraknath kristin A.persson Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Department of Physics University of CaliforniaBerkeleyCA 94720USA Department of Materials Science University of CaliforniaBerkeleyCA 94720USA

Band structures for electrons,phonons,and other quasiparticles are often an important aspect of describing the physical properties of periodic *** commonly,energy bands are computed along a one-dimensional path of high-symmetry points and line segments in reciprocal space(the“k-path”),which are assumed to pass through important features of the dispersion ***,existing methods for choosing this path rely on tabulated lists of high-symmetry points and line segments in the first Brillouin zone,determined using different symmetry criteria and unit cell *** we present a new“on-the-fly”symmetry-based approach to obtaining paths in reciprocal space that attempts to address the previous limitations of these *** a unit cell of a magnetic or nonmagnetic periodic solid,the site symmetry groups of points and line segments in the irreducible Brillouin zone are obtained from the total space *** elements in these groups are used alongside general and maximally inclusive high-symmetry criteria to choose segments for the final k-path.A smooth path connecting each segment is obtained using graph *** new framework not only allows for increased flexibility and user convenience but also identifies notable overlooked features in certain electronic band *** addition,a more intelligent and efficient method for analyzing magnetic materials is also enabled through proper accommodation of magnetic symmetry.

关键词： symmetry reciprocal approach

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

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npj Computational Materials 2016年第1期2卷 1-10页

作者： Bharat Medasani Anthony Gamst Hong Ding Wei Chen kristin A persson Mark Asta Andrew Canning Maciej Haranczyk Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Physical and Computational Sciences Directorate Pacific National Northwest LaboratoryRichlandWA 99354USA Biostatistics and Bioinformatics University of CaliforniaSan DiegoCA 92093USA Department of Materials Science&Engineering University of CaliforniaBerkeleyCA 94720USA Energy Technologies Area Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Department of Mechanical Materials and Aerospace EngineeringIllinois Institute of TechnologyChicagoIL 60616USA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA

We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic(A–B)compounds,using as an example systems with the cubic B2 crystal structure(with equiatomic AB stoichiometry).To the best of our knowledge,this work is the first application of machine learning-models for point defect *** throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic *** systems are classified into two groups:(i)those for which the intrinsic defects are antisites for both A and B rich compositions,and(ii)those for which vacancies are the dominant defect for either or both composition *** data was analyzed by machine learning-techniques using decision tree,and full and reduced multiple additive regression tree(MART)*** these three schemes,a reduced MART(r-MART)model using six descriptors(formation energy,minimum and difference of electron densities at the Wigner–Seitz cell boundary,atomic radius difference,maximal atomic number and maximal electronegativity)presents the highest fit(98%)and predictive(75%)*** model is used to predict the defect behavior of other B2 compounds,and it is found that 45%of the compounds considered feature vacancies as dominant defects for either A or B rich compositions(or both).The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds,and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.

关键词： intermetallic intermetallics defect

Enabling materials informatics for ^(29)Si solid-state NMR of crystalline materials

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npj Computational Materials 2020年第1期6卷 1219-1225页

作者： He Sun Shyam Dwaraknath Handong Ling Xiaohui Qu Patrick Huck kristin A.persson Sophia E.Hayes Department of Chemistry Washington UniversitySt.LouisMO 63130USA Lawrence Berkeley National Laboratory BerkeleyCA 94720USA Department of Materials Science and Engineering UC BerkeleyBerkeleyCA 94720USA

Nuclear magnetic resonance(NMR)spectroscopy is a powerful tool for obtaining precise information about the local bonding of materials,but difficult to interpret without a well-vetted dataset of reference *** ability to predict NMR parameters and connect them to three-dimensional local environments is critical for understanding more complex,long-range interactions.

关键词： spectra NMR crystalline

Effective mass and Fermi surface complexity factor from ab initio band structure calculations

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npj Computational Materials 2017年第1期3卷 417-423页

作者： Zachary M.Gibbs Francesco Ricci Guodong Li Hong Zhu kristin persson Gerbrand Ceder Geoffroy Hautier Anubhav Jain G.Jeffrey Snyder California Institute of Technology Division of Chemistry and Chemical EngineeringPasadenaCAUSA Institute of Condensed Matter and Nanosciences(IMCN) Universitécatholique de LouvainChemin desétoiles 8bte L7.03.01Louvain-la-NeuveBelgium Department of Materials Science and Engineering Northwestern University2220 Campus DriveEvanstonILUSA Department of Materials Science and Engineering Massachusetts Institute of Technology77 Massachusetts AvenueMassachusettsMAUSA Lawrence Berkeley National Lab 1 Cyclotron RoadBerkeleyCAUSA

The effective mass is a convenient descriptor of the electronic band structure used to characterize the density of states and electron transport based on a free electron *** effective mass is an excellent first-order descriptor in real systems,the exact value can have several definitions,each of which describe a different aspect of electron *** we use Boltzmann transport calculations applied to ab initio band structures to extract a density-of-states effective mass from the Seebeck Coefficient and an inertial mass from the electrical conductivity to characterize the band structure irrespective of the exact scattering *** identify a Fermi Surface Complexity Factor:N_(v)^(*)K^(*) from the ratio of these two masses,which in simple cases depends on the number of Fermi surface pockets eN_(v)^(*) T and their anisotropy K^(*),both of which are beneficial to high thermoelectric performance as exemplified by the high values found in *** Fermi Surface Complexity factor can be used in high-throughput search of promising thermoelectric materials.

关键词： mechanism materials Fermi