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| 1 | A high-throughput framework for determining adsorption energies on solid surfaces显示文摘In this work,we present a high-throughput workflow for calculation of adsorption energies on solid surfaces using density functional theory.Using open-source computational tools from the Materials Project infrastructure,we automate the procedure of constructing symmetrically distinct adsorbate configurations for arbitrary slabs.These algorithms are further used to construct and run workflows in a standard,automated way such that user intervention in the simulation procedure is minimal.To validate our approach,we compare results from our workflow to previous experimental and theoretical benchmarks from the CE27 database of chemisorption energies on solid surfaces.These 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 analysis.By 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. | Joseph H.Montoya Kristin A.Persson | 2017 | npj Computational Materials2017，，1： | 7 |
| 2 | Automated generation and ensemble-learned matching of X-ray absorption spectra显示文摘X-ray absorption spectroscopy(XAS)is a widely used materials characterization technique to determine oxidation states,coordination environment,and other local atomic structure information.Analysis of XAS relies on comparison of measured spectra to reliable reference spectra.However,existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage.In this work,we report the development of XASdb,a large database of computed reference XAS,and an Ensemble-Learned Spectra IdEntification(ELSIE)algorithm for the matching of spectra.XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra(XANES)for over 40,000 materials from the open-science Materials Project database.We discuss a high-throughput automation framework for FEFF calculations,built on robust,rigorously benchmarked parameters.FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra.We will demonstrate that the ELSIE algorithm,which combines 33 weak“learners”comprising a set of preprocessing steps and a similarity metric,can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures.The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project,providing an important new public resource for the analysis of XAS to all materials researchers.Finally,the ELSIE algorithm itself has been made available as part of veidt,an open source machine-learning library for materials science. | Chen Zheng Kiran Mathew Chi Chen Yiming Chen Hanmei Tang Alan Dozier Joshua J.Kas Fernando D.Vila John J.Rehr Louis F.J.Piper Kristin A.Persson Shyue Ping Ong | 2018 | npj Computational Materials2018，，1： | 3 |
| 3 | A charge-density-based general cation insertion algorithm for generating new Li-ion cathode materials显示文摘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 state.To 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 material.For 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 theory.In 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 guidance.Furthermore,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. | Jimmy-Xuan Shen Matthew Horton Kristin A.Persson | 2020 | npj Computational Materials2020，，1： | 2 |
| 4 | High-throughput predictions of metal-organic framework electronic properties:theoretical challenges,graph neural networks,and data exploration显示文摘With the goal of accelerating the design and discovery of metal–organic frameworks(MOFs)for electronic,optoelectronic,and energy storage applications,we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations.Compared to more accurate hybrid functionals,we find that the widely used PBE generalized gradient approximation(GGA)functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character.However,an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations.With regards to partial atomic charges,we find that different density functional approximations predict similar charges overall,although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference.Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes.We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work,paving the way for future high-throughput screening studies.To encourage exploration and reuse of the theoretical calculations presented in this work,the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project. | Andrew S.Rosen Victor Fung Patrick Huck Cody T.O’Donnell Matthew K.Horton Donald G.Truhlar Kristin A.Persson Justin M.Notestein Randall Q.Snurr | 2022 | npj Computational Materials2022，，1： | 1 |
| 5 | Evaluation of thermodynamic equations of state across chemistry and structure in the materials project显示文摘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 materials.Despite 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 judgment.In 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 literature.Our findings do not indicate a clear“best”EOS,but we identify three which consistently perform well relative to the rest of the set.Furthermore,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. | Katherine Latimer Shyam Dwaraknath Kiran Mathew Donald Winston Kristin A.Persson | 2018 | npj Computational Materials2018，，1： | 1 |
| 6 | Author Correction:Automated generation and ensemblelearned matching of X-ray absorption spectra显示文摘Correction to:npj Computational Materials http://gffzzd3cc09b8251d45dfsqcn9ocxf5vfq60cv.ffgz.tsg.suse.edu.cn/10.1038/s41524-018-0067-x,published online 20 March 2018 The following text has been added to the Acknowledgements section:“L.F.J.P.acknowledges support from the National Science Foundation(DMREF-1627583).” | Chen Zheng Kiran Mathew Chi Chen Yiming Chen Hanmei Tang Alan Dozier Joshua J.Kas Fernando D.Vila John J.Rehr Louis F.J.Piper Kristin A.Persson Shyue Ping Ong | 2018 | npj Computational Materials2018，，1： | 1 |
| 7 | An improved symmetry-based approach to reciprocal space path selection in band structure calculations显示文摘Band structures for electrons,phonons,and other quasiparticles are often an important aspect of describing the physical properties of periodic solids.Most 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 landscape.However,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 conventions.Here 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 conventions.Given 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 group.The 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 theory.This new framework not only allows for increased flexibility and user convenience but also identifies notable overlooked features in certain electronic band structures.In addition,a more intelligent and efficient method for analyzing magnetic materials is also enabled through proper accommodation of magnetic symmetry. | Jason M.Munro Katherine Latimer Matthew K.Horton Shyam Dwaraknath Kristin A.Persson | 2020 | npj Computational Materials2020，，1： | 0 |
| 8 | Enabling materials informatics for ^(29)Si solid-state NMR of crystalline materials显示文摘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 spectra.The ability to predict NMR parameters and connect them to three-dimensional local environments is critical for understanding more complex,long-range interactions. | He Sun Shyam Dwaraknath Handong Ling Xiaohui Qu Patrick Huck Kristin A.Persson Sophia E.Hayes | 2020 | npj Computational Materials2020，，1： | 0 |
| 9 | A flexible and scalable scheme for mixing computed formation energies from different levels of theory显示文摘Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory(DFT),which now contain millions of calculations at the generalized gradient approximation(GGA)level of theory.It is now feasible to carry out high-throughput calculations using more accurate methods,such as meta-GGA DFT;however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing(GGA)calculations.Instead,we propose here a general procedure by which higher-fidelity,low-coverage calculations(e.g.,meta-GGA calculations for selected chemical systems)can be combined with lower-fidelity,high-coverage calculations(e.g.,an existing database of GGA calculations)in a robust and scalable manner.We then use legacy PBE(+U)GGA calculations and new r2SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions,and discuss practical considerations for its implementation. | Ryan S.Kingsbury Andrew S.Rosen Ayush S.Gupta Jason M.Munro Shyue Ping Ong Anubhav Jain Shyam Dwaraknath Matthew K.Horton Kristin A.Persson | 2022 | npj Computational Materials2022，，1： | 0 |
| 10 | Topological graph-based analysis of solid-state ion migration显示文摘To accelerate the development of ion conducting materials,we present a general graph-theoretic analysis framework for ion migration in any crystalline structure.The nodes of the graph represent metastable sites of the migrating ion and the edges represent discrete migration events between adjacent sites.Starting 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 sites.Connected pathways in the periodic simulation cell corresponding to macroscopic ion migration are identified by searching for the lowest-cost cycle in the periodic migration graph.To 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. | Jimmy-Xuan Shen Haoming Howard Li Ann Rutt Matthew K.Horton Kristin A.Persson | 2023 | npj Computational Materials2023，，1： | 0 |
| 11 | High-throughput calculations of charged point defect properties with semi-local density functional theory— performance benchmarks for materials screening applications显示文摘Calculations of point defect energetics with Density Functional Theory(DFT)can provide valuable insight into several optoelectronic,thermodynamic,and kinetic properties.These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches.For applications of DFT-based high-throughput computation for data-driven materials discovery,point defect properties are of interest,yet are currently excluded from available materials databases.This work presents a benchmark analysis of automated,semi-local point defect calculations with a-posteriori corrections,compared to 245“gold standard”hybrid calculations previously published.We consider three different a-posteriori correction sets implemented in an automated workflow,and evaluate the qualitative and quantitative differences among four different categories of defect information:thermodynamic transition levels,formation energies,Fermi levels,and dopability limits.We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods,while also demonstrating the limits of quantitative accuracy. | Danny Broberg Kyle Bystrom Shivani Srivastava Diana Dahliah Benjamin A.D.Williamson Leigh Weston David O.Scanlon Gian-Marco Rignanese Shyam Dwaraknath Joel Varley Kristin A.Persson Mark Asta Geoffroy Hautier | 2023 | npj Computational Materials2023，，1： | 0 |
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