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| 1 | Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning显示文摘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 properties.High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds.The 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 ranges.The data was analyzed by machine learning-techniques using decision tree,and full and reduced multiple additive regression tree(MART)models.Among 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%)accuracy.This 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. | Bharat Medasani Anthony Gamst Hong Ding Wei Chen Kristin A Persson Mark Asta Andrew Canning Maciej Haranczyk | 2016 | npj Computational Materials2016，，1： | 6 |
| 2 | An efficient and accurate framework for calculating lattice thermal conductivity of solids:AFLOW-AAPL Automatic Anharmonic Phonon Library显示文摘One of the most accurate approaches for calculating lattice thermal conductivity,κ_(l),is solving the Boltzmann transport equation starting from third-order anharmonic force constants.In addition to the underlying approximations of ab-initio parameterization,two main challenges are associated with this path:high computational costs and lack of automation in the frameworks using this methodology,which affect the discovery rate of novel materials with ad-hoc properties.Here,the Automatic Anharmonic Phonon Library(AAPL)is presented.It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis,it solves the Boltzmann transport equation to obtain κ_(l),and allows a fully integrated operation with minimum user intervention,a rational addition to the current high-throughput accelerated materials development framework AFLOW.An“experiment vs.theory”study of the approach is shown,comparing accuracy and speed with respect to other available packages,and for materials characterized by strong electron localization and correlation.Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements. | Jose J.Plata Pinku Nath Demet Usanmaz Jesus Carrete Cormac Toher Maarten de Jong Mark Asta Marco Fornari Marco Buongiorno Nardelli Stefano Curtarolo | 2017 | npj Computational Materials2017，，1： | 5 |
| 3 | Ab initio modeling of the energy landscape for screw dislocations in body-centered cubic high-entropy alloys显示文摘In traditional body-centered cubic(bcc)metals,the core properties of screw dislocations play a critical role in plastic deformation at low temperatures.Recently,much attention has been focused on refractory high-entropy alloys(RHEAs),which also possess bcc crystal structures.However,unlike face-centered cubic high-entropy alloys(HEAs),there have been far fewer investigations into bcc HEAs,specifically on the possible effects of chemical short-range order(SRO)in these multiple principal element alloys on dislocation mobility.Here,using density functional theory,we investigate the distribution of dislocation core properties in MoNbTaW RHEAs alloys,and how they are influenced by SRO.The average values of the core energies in the RHEA are found to be larger than those in the corresponding pure constituent bcc metals,and are relatively insensitive to the degree of SRO.However,the presence of SRO is shown to have a large effect on narrowing the distribution of dislocation core energies and decreasing the spatial heterogeneity of dislocation core energies in the RHEA.It is argued that the consequences of the mechanical behavior of HEAs is a change in the energy landscape of the dislocations,which would likely heterogeneously inhibit their motion. | Sheng Yin Jun Ding Mark Asta Robert O.Ritchie | 2020 | npj Computational Materials2020，，1： | 4 |
| 4 | Spatial correlation of elastic heterogeneity tunes the deformation behavior of metallic glasses显示文摘Metallic glasses(MGs)possess remarkably high strength but often display only minimal tensile ductility due to the formation of catastrophic shear bands.Purposely enhancing the inherent heterogeneity to promote distributed flow offers new possibilities in improving the ductility of monolithic MGs.Here,we report the effect of the spatial heterogeneity of elasticity,resulting from the inherently inhomogeneous amorphous structures,on the deformation behavior of MGs,specifically focusing on the ductility using multiscale modeling methods.A highly heterogeneous,Gaussian-type shear modulus distribution at the nanoscale is revealed by atomistic simulations in Cu_(64)Zr_(36) MGs,in which the soft population of the distribution exhibits a marked propensity to undergo the inelastic shear transformation.By employing a mesoscale shear transformation zone dynamics model,we find that the organization of such nanometer-scale shear transformation events into shear-band patterns is dependent on the spatial heterogeneity of the local shear moduli.A critical spatial correlation length of elastic heterogeneity is identified for the simulated MGs to achieve the best tensile ductility,which is associated with a transition of shear-band formation mechanisms,from stress-dictated nucleation and growth to structure-dictated strain percolation,as well as a saturation of elastically soft sites participating in the plastic flow.This discovery is important for the fundamental understanding of the role of spatial heterogeneity in influencing the deformation behavior of MGs.We believe that this can facilitate the design and development of new ductile monolithic MGs by a process of tuning the inherent heterogeneity to achieve enhanced ductility in these high-strength metallic alloys. | Neng Wang Jun Ding Feng Yan Mark Asta Robert O.Ritchie Lin Li | 2018 | npj Computational Materials2018，，1： | 4 |
| 5 | Cognitive interventions to reduce diagnostic error: a narrative review显示文摘 | Mark L Graber Stephanie Kissam Velma L Payne Ashley N D Meyer Asta Sorensen Nancy Lenfestey Elizabeth Tant Kerm Henriksen Kenneth LaBresh Hardeep Singh | 2012 | BMJ Quality & Safety2012，，7： | 1 |
| 6 | Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy显示文摘While rhenium is an ideal material for rapid thermal cycling applications under high temperatures,such as rocket engine nozzles,its high cost limits its widespread use and prompts an exploration of viable cost-effective substitutes.In prior work,we identified a promising pool of candidate substitute alloys consisting of Mo,Ru,Ta,and W.In this work we demonstrate,based on density functional theory melting temperature calculations,that one of the candidates,Mo_(0.292)Ru_(0.555)Ta_(0.031)W_(0.122),exhibits a high melting temperature(around 2626 K),thus supporting its use in high-temperature applications. | Qi-Jun Hong Jan Schroers Douglas Hofmann Stefano Curtarolo Mark Asta Axel van de Walle | 2021 | npj Computational Materials2021，，1： | 1 |
| 7 | Quantum effects on dislocation motion from ring-polymer molecular dynamics显示文摘Quantum motion of atoms known as zero-point vibration was recently proposed to explain a long-standing discrepancy between theoretically computed and experimentally measured low-temperature plastic strength of iron and possibly other metals with high atomic masses.This finding challenges the traditional notion that quantum motion of atoms is relatively unimportant in solids comprised of heavy atoms.Here we report quantum dynamic simulations of quantum effects on dislocation motion within the exact formalism of Ring-Polymer Molecular Dynamics(RPMD).To extend the reach of quantum atomistic simulations to length and time scales relevant for extended defects in materials,we implemented RPMD in the open-source code LAMMPS thus making the RPMD method widely available to the community.We use our RPMD/LAMMPS approach for direct calculations of dislocation mobility and its effects on the yield strength ofα-iron.Our simulation results establish that quantum effects are noticeable at temperatures below 50 K but account for only a modest(≈13% at T=0 K)overall reduction in the Peierls barrier,at variance with the factor of two reduction predicted earlier based on the more approximate framework of harmonic transition state theory.Our results confirm that zero-point vibrations provide ample additional agitation for atomic motion that increases with decreasing temperature,however its enhancing effect on dislocation mobility is largely offset by an increase in the effective atom size,an effect known as quantum dispersion that has not been accounted for in the previous calculations. | Rodrigo Freitas Mark Asta Vasily V.Bulatov | 2018 | npj Computational Materials2018，，1： | 1 |
| 8 | Modeling antiphase boundary energies of Ni_(3)Al-based alloys using automated density functional theory and machine learning显示文摘Antiphase boundaries(APBs)are planar defects that play a critical role in strengthening Ni-based superalloys,and their sensitivity to alloy composition offers a flexible tuning parameter for alloy design.Here,we report a computational workflow to enable the development of sufficient data to train machine-learning(ML)models to automate the study of the effect of composition on the(111)APB energy in Ni_(3)Al-based alloys.We employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m^(−2).We demonstrate the transferability of these models by predicting APB energies in commercial superalloys.Moreover,our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy. | Enze Chen Artur Tamm Tao Wang Mario E.Epler Mark Asta Timofey Frolov | 2022 | npj Computational Materials2022，，1： | 0 |
| 9 | Realistic magnetic thermodynamics by local quantization of a semiclassical Heisenberg model显示文摘Classical Monte Carlo simulation of the Heisenberg model poorly describes many thermodynamic phenomena due to its neglect of the quantum nature of spins.Alternatively,we discuss how to semiclassically approach the quantum problem and demonstrate a simple method for introducing a locally approximate form of spin quantization.While the procedure underestimates magnetic short-range order,our results suggest a simple correction for recovering realistic spin–spin correlations above the critical temperature.Moreover,ensemble fluctuations are found to provide reasonably accurate thermodynamics,largely reproducing quantum mechanically calculated heat capacities and experimental magnetometry for ferromagnetic Fe and antiferromagnetic RbMnF3.Extensions of the method are proposed to address remaining inaccuracies. | Flynn Walsh Mark Asta Lin-Wang Wang | 2022 | npj Computational Materials2022，，1： | 0 |
| 10 | Real time imaging of two-dimensional iron oxide spherulite nanostructure formation显示文摘The formation of complex hierarchical nanostructures has attracted a lot of attention from both the fundamental science and potential applications point of view.Spherulite structures with radial fibrillar branches have been found in various solids;however,their growth mechanisms remain poorly understood.Here,we report real time imaging of the formation of two-dimensional(2D)iron oxide spherulite nanostructures in a liquid cell using transmission electron microscopy(TEM).By tracking the growth trajectories,we show the characteristics of the reaction front and growth kinetics.Our observations reveal that the tip of a growing branch splits as the width exceeds certain sizes(5.5–8.5 nm).The radius of a spherulite nanostructure increases linearly with time at the early stage,transitioning to nonlinear growth at the later stage.Furthermore,a thin layer of solid is accumulated at the tip and nanoparticles from secondary nucleation also appear at the growing front which later develop into fibrillar branches.The spherulite nanostructure is polycrystalline with the co-existence of ferrihydrite and Fe3O4 through-out the growth.A growth model is further established,which provides rational explanations on the linear growth at the early stage and the nonlinearity at the later stage of growth. | Wenjing Zheng Matthew R.Hauwiller Wen-I Liang Colin Ophus Peter Ercius Emory M.Chan Ying-Hao Chu Mark Asta Xiwen Du A.Paul Alivisatos Haimei Zheng | 2019 | Nano Research2019，12，11： | 0 |
| 11 | Grain boundary effects in high-temperature liquid-metal dealloying: a multi-phase field study显示文摘A multi-phase field model is employed to study the microstructural evolution of an alloy undergoing liquid dealloying,specifically considering the role of grain boundaries.A semi-implicit time-stepping algorithm using spectral methods is implemented,which enables simulating large 2D and 3D domains over long time scales while still maintaining a realistic interfacial thickness.Simulations reveal a mechanism of coupled grain–boundary migration to maintain equilibrium contact angles with the topologically complex solid–liquid interface,which locally accelerates diffusion-coupled growth of a liquid channel into the precursor.This mechanism asymmetrically disrupts the ligament connectivity of the dealloyed structure in qualitative agreement with published experimental observations.The grain boundary migration-assisted corrosion channels form even for precursors with small amounts of the dissolving alloy species,below the parting limit.The activation of this grain boundary dealloying mechanism depends strongly on grain boundary mobility. | Nathan Bieberdorf Mark Asta Laurent Capolungo | 2023 | npj Computational Materials2023，，1： | 0 |
| 12 | 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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