Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys

Refractory multi-principal element alloys(RMPEAs)are promising materials for high-temperature structural applications.Here,we investigate the role of short-range ordering(SRO)on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach.Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies(USFEs).From mesoscale phase-field dislocation dynamics simulations,we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide.The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations,with higher SRO decreasing the degree of USFE dispersion and hence,amount of hardening.Finally,we show how the morphology of an expanding dislocation loop is affected by the applied stress.

Recent advances and applications of deep learning methods in materials science

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.In contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods.In this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language processing.For each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and datasets.We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

An electrostatic spectral neighbor analysis potential for lithium nitride

Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy.However,a challenge in their application to ionic systems is the treatment of long-ranged electrostatics.Here,we present a highly accurate electrostatic Spectral Neighbor Analysis Potential(eSNAP)for ionicα-Li3N,a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries.We show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces,as well as various properties,such as lattice constants,elastic constants,and phonon dispersion curves.We also demonstrate the application of eSNAP in long-time,large-scale Li diffusion studies in Li3N,providing atomistic insights into measures of concerted ionic motion(e.g.,the Haven ratio)and grain boundary diffusion.This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism,enabling large-scale atomistic simulations for such systems.

Complex strengthening mechanisms in the NbMoTaW multiprincipal element alloy

Refractory multi-principal element alloys(MPEAs)have exceptional mechanical properties,including high strength-to-weight ratio and fracture toughness,at high temperatures.Here we elucidate the complex interplay between segregation,short-range order,and strengthening in the NbMoTaW MPEA through atomistic simulations with a highly accurate machine learning interatomic potential.In the single crystal MPEA,we find greatly reduced anisotropy in the critically resolved shear stress between screw and edge dislocations compared to the elemental metals.In the polycrystalline MPEA,we demonstrate that thermodynamically driven Nb segregation to the grain boundaries(GBs)and W enrichment within the grains intensifies the observed short-range order(SRO).The increased GB stability due to Nb enrichment reduces the von Mises strain,resulting in higher strength than a random solid solution MPEA.These results highlight the need to simultaneously tune GB composition and bulk SRO to tailor the mechanical properties of MPEAs.

AtomSets as a hierarchical transfer learning framework for small and large materials datasets

Predicting properties from a material’s composition or structure is of great interest for materials design.Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data.However,deep learning models suffer in the small data regime that is common in materials science.Here we develop the AtomSets framework,which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data(<400)and large structural data(>130,000).The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits.They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits.The models require minimal domain knowledge inputs and are free from feature engineering.The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.

Automated generation and ensemble-learned matching of X-ray absorption spectra

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.

Author Correction:Automated generation and ensemblelearned matching of X-ray absorption spectra

Correction to:npj Computational Materials https://doi.org/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).”

A flexible and scalable scheme for mixing computed formation energies from different levels of theory

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.