A Volt-Second Source for Calibration of Integrator in a Pulsed Field Magnetometer

A volt-second （Vs） source intended for absolutely calibrating the integrator in a pulsed field magnetometer （PFM） is designed and proven to be with accurate rising and falling edges and reasonable lower uncertainty. A comparison experiment shows that the difference between the magnetic fluxes generated respectively by the Vs source and the mutual inductor is within ±0.04%. The PFM is then calibrated in an absolute way of the Vs source. The calibrated PFM gives the measured results in good agreement with a static BH tracer supplied by National Institute of Metrology of China and provides a convenient way of studying the effect of mathematic process on the dynamic measuring curve of PFMs.

Imaging the diffusion pathway of Al^3+ ion in NASICON-type (Al0.2Zr0.8)20/19Nb(PO4)3 as electrolyte for rechargeable solid-state Al batteries

Among all-solid-state batteries, rechargeable Al-ion batteries have attracted most attention because they involve threeelectron-redox reactions with high theoretic specific capacity. However, the solid Al-ion conductor electrolytes are less studied. Here, the microscopic path of Al3+-ion conduction of NASICON-type(Al0.2Zr0.8)20/19Nb(PO4)3oxide is identified by temperature-dependent neutron powder diffraction and aberration-corrected scanning transmission electron microscopy experiments.(Al0.2Zr0.8)20/19Nb(PO4)3shows a rhombohedral structure consisting of a framework of(Zr,Nb)O6octahedra sharing corners with(PO4) tetrahedra; the Al occupy trigonal antiprisms exhibiting extremely large displacement factors. This suggests a strong displacement of Al ions along the c axis of the unit cell as they diffuse across the structure by a vacancy mechanism. Negative thermal expansion behavior is also identified along a and b axes, due to folding of the framework as temperature increases.

A critical examination of robustness and generalizability of machine learning prediction of materials properties

Recent advances in machine learning(ML)have led to substantial performance improvement in material database benchmarks,but an excellent benchmark score may not imply good generalization performance.Here we show that ML models trained on Materials Project 2018 can have severely degraded performance on new compounds in Materials Project 2021 due to the distribution shift.We discuss how to foresee the issue with a few simple tools.Firstly,the uniform manifold approximation and projection(UMAP)can be used to investigate the relation between the training and test data within the feature space.Secondly,the disagreement between multiple ML models on the test data can illuminate out-of-distribution samples.We demonstrate that the UMAP-guided and query by committee acquisition strategies can greatly improve prediction accuracy by adding only 1%of the test data.We believe this work provides valuable insights for building databases and models that enable better robustness and generalizability.

Atomistic Line Graph Neural Network for improved materials property predictions

Graph neural networks(GNN)have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.While most existing GNN models for atomistic predictions are based on atomic distance information,they do not explicitly incorporate bond angles,which are critical for distinguishing many atomic structures.Furthermore,many material properties are known to be sensitive to slight changes in bond angles.We present an Atomistic Line Graph Neural Network(ALIGNN),a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles.We demonstrate that angle information can be explicitly and efficiently included,leading to improved performance on multiple atomistic prediction tasks.We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT,Materials project,and QM9 databases.ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks by up to 85%in accuracy with better or comparable model training speed.

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment.

New frontiers for the materials genome initiative

The Materials Genome Initiative(MGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI.In May 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the MGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

Advances in shape measurement in the digital world

The importance of particle shape in terms of its effects on the behaviour of powders and other particulate systems has long been recognised, but particle shape information has been rather difficult to obtain and use until fairly recently, unlike its better-known counterpart, particle size. However, advances in computing power and 3D image acquisition and analysis techniques have resulted in major progress being made in the measurement, description and application of particle shape information in recent years. Because we are now in a digital era, it is fitting that many of these advanced techniques are based on digital technology. This review article aims to trace the development of these new techniques, highlight their contributions to both academic and practical applications, and present a perspective for future developments.

Does friction contribute to formability improvement using servo press?

Servo press forming machines are advanced forming systems that are capable of imparting interrupted punch motion,resulting in enhanced room temperature formability.The exact mechanism of the formability improvement is not yet established.The contribution of interrupted motion in the ductility improvement has been studied through stress relaxation phenomena in uniaxial tensile(UT)tests.However,the reason for improved formability observed when employing servo press is complicated due to the additional contribution from frictional effects.In the present work,an attempt is made to decouple the friction effect on formability improvement numerically.The improved formability is studied using a hole expansion test(HET).The limit of forming during hole expansion is modeled using the Hosford–Coulomb(HC)damage criteria,which is implemented as a user subroutine in a commercial explicit finite element(FE)software.Only the contribution of stress relaxation is accounted for in the evolution of the damage variable during interrupted loading.Therefore,the difference between simulation and experimental hole expansion ratio(HER)can be used to decouple the friction effect from the overall formability improvement during hole expansion.The improvement in HER due to stress relaxation and friction effect is different.The study showed that the model effectively captures the hole expansion deformation process in both monotonic and interrupted loading conditions.Compared to stress relaxation,friction effect played a major role during interrupted HET.

An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Materials design aims to identify the material features that provide optimal properties for various engineering applications,such as aerospace,automotive,and naval.One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties.This paper proposes an end-to-end artificial intelligence(AI)-driven microstructure optimization framework for elastic properties of materials.In this work,the microstructure is represented by the Orientation Distribution Function(ODF)that determines the volume densities of crystallographic orientations.The framework was evaluated on two crystal systems,cubic and hexagonal,for Titanium(Ti)in Joint Automated Repository for Various Integrated Simulations(JARVIS)database and is expected to be widely applicable for materials with multiple crystal systems.The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy(STEM)at high speed,with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods.Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way.However,like many other tasks related to object detection and identification in artificial intelligence,it is challenging to detect and identify defects from STEM images.Furthermore,it is difficult to deal with crystal structures that have many atoms and low symmetries.Previous methods used for defect detection and classification were based on supervised learning,which requires human-labeled data.In this work,we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine(OCSVM).We introduce two schemes of image segmentation and data preprocessing,both of which involve taking the Patterson function of each segment as inputs.We demonstrate that this method can be applied to various defects,such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

Prediction of Weyl semimetal and antiferromagnetic topological insulator phases in Bi_(2)MnSe_(4)

Three-dimensional materials with strong spin–orbit coupling and magnetic interactions represent an opportunity to realize a variety of rare and potentially useful topological phases with broken time-reversal symmetry.In this work,we use first principles calculations to show that the recently synthesized material Bi_(2)MnSe_(4) displays a combination of spin–orbit-induced band inversion,also observed in non-magnetic topological insulator Bi2PbSe4,with magnetic interactions,leading to several topological phases.In bulk form,the ferromagnetic phase of Bi_(2)MnSe_(4) has symmetry protected band crossings at the Fermi level,leading to either a nodal line or Weyl semimetal,depending on the direction of the spins.Due to the combination of time reversal symmetry plus a partial translation,the ground state layered antiferromagnetic phase is instead an antiferromagnetic topological insulator.The surface of this phase intrinsically breaks time-reversal symmetry,allowing the observation of the half-integer quantum anomalous Hall effect.Furthermore,we show that in thin film form,for sufficiently thick slabs,Bi_(2)MnSe_(4) becomes a Chern insulator with a band gap of up to 58 meV.This combination of properties in a stoichiometric magnetic material makes Bi_(2)MnSe_(4) an excellent candidate for displaying robust topological behavior.

Designing high-TC superconductors with BCS-inspired screening,density functional theory,and deep-learning

We develop a multi-step workflow for the discovery of conventional superconductors,starting with a Bardeen–Cooper–Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states.Next,we perform electron-phonon coupling calculations for 1058 of them to establish a large and systematic database of BCS superconducting properties.Using the McMillan-Allen-Dynes formula,we identify 105 dynamically stable materials with transition temperatures,TC≥5 K.Additionally,we analyze trends in our dataset and individual materials including MoN,VC,VTe,KB_(6),Ru_(3)NbC,V_(3)Pt,ScN,LaN_(2),RuO_(2),and TaC.We demonstrate that deep-learning(DL)models can predict superconductor properties faster than direct first-principles computations.Notably,we find that by predicting the Eliashberg function as an intermediate quantity,we can improve model performance versus a direct DL prediction of TC.We apply the trained models on the crystallographic open database and pre-screen candidates for further DFT calculations.

Predicting strength distributions of MEMS structures using flaw size and spatial density

The populations of flaws in individual layers of microelectromechanical systems(MEMS)structures are determined and verified using a combination of specialized specimen geometry,recent probabilistic analysis,and topographic mapping.Strength distributions of notched and tensile bar specimens are analyzed assuming a single flaw population set by fabrication and common to both specimen geometries.Both the average spatial density of flaws and the flaw size distribution are determined and used to generate quantitative visualizations of specimens.Scanning probe-based topographic measurements are used to verify the flaw spacings determined from strength tests and support the idea that grain boundary grooves on sidewalls control MEMS failure.The findings here suggest that strength controlling features in MEMS devices increase in separation,i.e.,become less spatially dense,and decrease in size,i.e.,become less potent flaws,as processing proceeds up through the layer stack.The method demonstrated for flaw population determination is directly applicable to strength prediction for MEMS reliability and design.

Mechanisms oftoothdamageand Paranthropus dietary reconstruction

According to the current fossil record,the extinct hominin genus Paranthropus and the genus Homo both first appeared~2.7 million years ago.Despite this similarity in geological age,Paranthropus evolved enormous postcanine teeth with very thick enamel while Homo evolved smaller teeth.Results from contact mechanics mode ls derived from mu ltiple scales of tooth damage(microwear,macrowear,and fracture)are reviewed to examine this evolutionary divergence and the role that diet may have played in it.Each scale of investigation reveals different kinds of evide nce that can be combined into a more complete picture of hominin diet and feeding beh aviour.Micr owear reveals information about recent feeding events,while macrowear and fr acture record longer-term trends.The synthesis of all three levels of evidence exposes significant dietary diversity,not only between these two hominin genera but within them as well.Within Paranthropus,the eastern and southern African species(P.boisei and P.robustus,respectively)were morphologically similar but appear to have been functionally different.Whereas P.boisei apparently used its teeth to consume large quantities of low qua lity vegetation,P.robustus had a more varied diet that included harder objects,possibly items such as seeds,nuts,or underground storage organs.