arXiv subjects

Shyue Ping Ong

Publications and source records attributed to Shyue Ping Ong.

At least 37 records · Page 2Linked to original sources

arXiv2022

Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolytes

Oxide solid electrolytes (OSEs) have the potential to achieve improved safety and energy density for lithium-ion batteries, but their high grain-boundary (GB) resistance is a general bottleneck. In the most well studied perovskite OSE, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of GBs is about three orders of magnitude lower than that of the bulk. In contrast, the related Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low GB resistance for reasons yet unknown. Here, we used aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the atomic scale structure and composition of LSTZ0.75 GBs. Vibrational electron energy loss spectroscopy is applied for the first time to characterize the otherwise unmeasurable Li distribution in GBs of LSTZ0.75. We found that Li depletion, which is a major reason for the low GB ionic conductivity of LLTO, is absent for the GBs of LSTZ0.75. Instead, the low GB resistivity of LSTZ0.75 is attributed to the formation of a unique defective cubic perovskite interfacial structure that contained abundant vacancies. Our study provides insights into the atomic scale mechanisms of low GB resistivity and sheds light on possible paths for designing OSEs with high total ionic conductivity.

cond-mat.mtrl-sci↗

arXiv2022

Multi-scale Investigation of Chemical Short-Range Order and Dislocation Glide in the MoNbTi and TaNbTi Refractory Multi-Principal Element Alloys

Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of chemical short-range ordering (CSRO) on dislocation glide in two model RMPEAs - TaNbTi and MoNbTi - using a multi-scale modeling approach. A highly accurate machine learning interatomic potential was developed for the Mo-Ta-Nb-Ti system and used to demonstrate 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 (USFE). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs, thereby increasing the 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, with higher SRO requiring higher applied stresses to achieve smooth screw dislocation glide.

cond-mat.mtrl-sci↗

arXiv2022

A Universal Machine Learning Model for Elemental Grain Boundary Energies

The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small $Σ$ ($Σ< 10$) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m$^{-2}$. More importantly, this universal GB energy model can be extrapolated to the energies of high $Σ$ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science.

cond-mat.mtrl-sci↗

arXiv2021

Oxygen deficiency and migration mediated electric polarization in Fe,Co-substituted SrTiO$_{3-δ}$

We use density functional theory (DFT) calculations to show that oxygen vacancies ($v_\mathrm{O}$) and mobility induce noncentrosymmetric polar structures in SrTi$_{1-x-y}$Fe$_{x}$Co$_{y}$O$_{3-δ}$ ($x=y=0.125$) with $δ= \{0.125, 0.25\}$, enhance the saturation magnetization and give rise to large changes in the electric polarization $\vertΔP\vert$. We present an intuitive set of rules for SrTiFeCoO$_{3-δ}$ (STFC), which are based on the interplay between (Co/Fe)-$v_\mathrm{O}$ defects, magnetic cations coordination and topological vacancy disorder. STFC structures convey layered crystals with sheets of linear organized O$_{4,5,6}$-coordinated Fe-Co pairs, sandwiched with layers of O$_{5}$-coordinated Ti. Co,Fe-$v_\mathrm{O}$ defects are the source of the crystal distortions, cations off-centering and bending of the oxygen octahedra, which added to the charge redistribution mediated by $v_\mathrm{O}$, the cations electronegativity and valence states trigger an effective electric polarization. Oxygen migrations for $δ=0.125$ provides us with $\vertΔ\mathbf{P}\vert$ $>\sim10 μ$C/cm$^2$ due to a quantum-of-polarization differences between $δ=0.125$ structures. Increasing the deficiency to $δ=0.25$ yields $\vertΔ\mathbf{P}\vert$ whose O-migration resolved polarization for $δ=0.25$ is $>\sim3 μ$C/cm$^2$ in the worst case scenario. Magnetism is dominated by the Fe,Co spin states for $δ=0.125$ while there is a raid of Ti magnetic moments ($\sim1μ_{B}$) for $δ=0.25$. Magnetic and electric order parameters change for variations of $δ$ or oxygen migrations for a given deficiency. Our results capture characteristics observed in the end-members of the series SrTi(Co,Fe)O$_{3}$, and suggest the existence of a broader set of rules for oxygen deficient multiferroic oxides.

cond-mat.mtrl-sci↗

arXiv2021

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. 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. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.

cond-mat.mtrl-sci↗

arXiv2021

Emergence of near-boundary segregation zones in face-centered cubic multi-principal element alloys

Grain boundaries have been shown to dramatically influence the behavior of relatively simple materials such as monatomic metals and binary alloys. The increased chemical complexity associated with multi-principal element alloys is hypothesized to lead to new grain boundary phenomena. To explore the relationship between grain boundary structure and chemistry in these materials, hybrid molecular dynamics/Monte Carlo simulations of a faceted Σ11 <110> tilt boundary, chosen to sample both high- and low-energy boundary configurations, are performed in face-centered cubic CrFeCoNiCu and CrFeCoNi equiatomic alloys. Unexpected enrichment of Fe is discovered in the face-centered cubic regions adjacent to the interface and found to be correlated with a structurally-distinct region of reduced atomic volume. Comparison with the boundary of the same type in monatomic Cu demonstrates that altered near-boundary regions exist in simpler systems as well, with the chemical complexity of the multi-principal element alloys highlighting its existence and importance.

cond-mat.mtrl-sci↗

arXiv2021

Proton distribution visualization in perovskite nickelate devices utilizing nanofocused X-rays

We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO$_3$-based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-functional theory (DFT) simulations show partial reduction of nickel valence in the region with high proton concentration, which leads to the insulating behavior. Concurrently, x-ray diffraction reveals only a small lattice distortion in the doped regions. Together, our results directly show that the knob which proton doping modifies is the electronic valency, and not the crystal lattice. The studies are relevant to on-going efforts to disentangle structural and electronic effects across metal-insulator phase transitions in correlated oxides.

cond-mat.mtrl-sci↗

arXiv2021

Bridging the Gap Between Simulated and Experimental Ionic Conductivities in Lithium Superionic Conductors

Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. While $ab$ $initio$ molecular dynamics have been extensively applied to study these materials, there are often large discrepancies between predicted and experimentally measured ionic conductivities and activation energies due to the high temperatures and short time scales of such simulations. Here, we present a strategy to bridge this gap using moment tensor potentials (MTPs). We show that MTPs trained on energies and forces computed using the van der Waals optB88 functional yield much more accurate lattice parameters, which in turn leads to accurate prediction of ionic conductivities and activation energies for the Li$_{0.33}$La$_{0.56}$TiO$_3$, Li$_3$YCl$_6$ and Li$_7$P$_3$S$_{11}$ LSCs. NPT MD simulations using the optB88 MTPs also reveal that all three LSCs undergo a transition between two quasi-linear Arrhenius regimes at relatively low temperatures. This transition can be traced to an expansion in the number and diversity of diffusion pathways, in some cases with a change in the dimensionality of diffusion. This work presents not only an approach to develop high accuracy MTPs, but also outlines the diffusion characteristics for LSCs which is otherwise inaccessible through $ab$ $initio$ computation.

cond-mat.mtrl-sci↗

arXiv2021

Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning

Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive density functional theory (DFT) calculations, which limits ML-based exploration to either known crystals or a small number of hypothetical crystals. Here, we demonstrate that the application of Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform "DFT-free" relaxations of crystal structures. Using this approach to significantly improve the accuracy of ML-predicted formation energies and elastic moduli of hypothetical crystals, two novel ultra-incompressible hard materials MoWC2 (P63/mmc) and ReWB (Pca21) were identified and successfully synthesized via in-situ reactive spark plasma sintering from a screening of 399,960 transition metal borides and carbides. This work addresses a critical bottleneck to accurate property predictions for hypothetical materials, paving the way to ML-accelerated discovery of new materials with exceptional properties.

cond-mat.mtrl-sci↗

arXiv2021

AtomSets -- A Hierarchical Transfer Learning Framework for Small and Large Materials Datasets

Predicting materials properties from composition or structure is of great interest to the materials science community. 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 leverage the transfer learning concept and the graph network deep learning framework and develop the AtomSets machine learning framework for consistent high model accuracy at both small and large materials data. The AtomSets models can work with both compositional and structural materials data. By combining with transfer learned features from graph networks, they can achieve state-of-the-art accuracy from using small compositional data (<400) to large structural data (>130,000). The AtomSets models show much lower errors than the state-of-the-art graph network models at small data limits and the classical machine learning models at large data limits. They also transfer better in the 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 opens new routes for machine learning-assisted materials design and discovery.

cond-mat.mtrl-sci↗

arXiv2021

Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties, their practical utility is limited by the scarcity of high-fidelity data. Here, we develop multi-fidelity graph networks as a universal approach to achieve accurate predictions of materials properties with small data sizes. As a proof of concept, we show that the inclusion of low-fidelity Perdew-Burke-Ernzerhof band gaps greatly enhances the resolution of latent structural features in materials graphs, leading to a 22-45\% decrease in the mean absolute errors of experimental band gap predictions. We further demonstrate that learned elemental embeddings in materials graph networks provide a natural approach to model disorder in materials, addressing a fundamental gap in the computational prediction of materials properties.

cond-mat.mtrl-sci↗

arXiv2020

The Breakdown of Mott Physics at VO$_2$ Surfaces

Transition metal oxides such as vanadium dioxide (VO$_2$), niobium dioxide (NbO$_2$), and titanium sesquioxide (Ti$_2$O$_3$) are known to undergo a temperature-dependent metal-insulator transition (MIT) in conjunction with a structural transition within their bulk. However, it is not typically discussed how breaking crystal symmetry via surface termination affects the complicated MIT physics. Using synchrotron-based x-ray spectroscopy, low energy electron diffraction (LEED), low energy electron microscopy (LEEM), transmission electron microscopy (TEM), and several other experimental techniques, we show that suppression of the bulk structural transition is a common feature at VO$_2$ surfaces. Our density functional theory (DFT) calculations further suggest that this is due to inherent reconstructions necessary to stabilize the surface, which deviate the electronic structure away from the bulk d$^1$ configuration. Our findings have broader ramifications not only for the characterization of other "Mott-like" MITs, but also for any potential device applications of such materials.

cond-mat.str-el↗

arXiv2020

Complex Strengthening Mechanisms in the NbMoTaW Multi-Principal 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.

cond-mat.mtrl-sci↗

arXiv2020

Unified Theory of Thermal Quenching in Inorganic Phosphors

We unify two prevailing theories of thermal quenching (TQ) in rare-earth-activated inorganic phosphors - the cross-over and auto-ionization mechanisms - into a single predictive model. Crucially, we have developed computable descriptors for activator environment stability from ab initio molecular dynamics simulations to predict TQ under the cross-over mechanism, which can be augmented by a band gap calculation to account for auto-ionization. The resulting TQ model predicts the experimental TQ in 29 known phosphors to within ~ 3-8%. Finally, we have developed an efficient topological approach to rapidly screen vast chemical spaces for the discovery of novel, thermally robust phosphors.

cond-mat.mtrl-sci↗

arXiv2020

Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams: Addressing the Challenge of Five Degrees of Freedom

Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, a potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called "complexion diagrams," as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a "mission impossible" to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ~108 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.

cond-mat.mtrl-sci↗

arXiv2019

Random Forest Models for Accurate Identification of Coordination Environments from X-ray Absorption Near-Edge Structure

Analyzing coordination environments using X-ray absorption spectroscopy has broad applications ranging from solid-state physics to material chemistry. Here, we show that random forest models can identify the main coordination environment from K-edge X-ray absorption near edge structure (XANES) with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine learning (ML) models. In a departure from prior works, we used a robust description of the coordination environment as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1-12. The random forest models were trained on the world's largest database of ~190,000 computed K-edge XANES spectra. Furthermore, the random forest models can be applied to predict the coordination environment from experimental K-edge XANES with minimal loss in accuracy (82.1%) due to the use of data augmentation. A drop-out feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification, with the post-peak region becoming increasingly important at higher coordination numbers. This work provides a general strategy to identify the coordination environment from K-edge XANES across broad chemistries, paving the way for future advancements in the application of ML to spectroscopy.

cond-mat.mtrl-sci↗

arXiv2019

A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of ML-IAPs based on four local environment descriptors --- Behler-Parrinello symmetry functions, smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors --- using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model, and consequently computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

physics.comp-ph↗

arXiv2019

Grain Boundary Properties of Elemental Metals

The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain Boundary Database (GBDB), the largest database of DFT-computed grain boundary properties to date. The database currently encompasses 327 GBs of 58 elemental metals, including 10 common twist or symmetric tilt GBs for body-centered cubic (bcc) and face-centered cubic (fcc) systems and the $Σ$7 [0001] twist GB for hexagonal close-packed (hcp) systems. In particular, we demonstrate a novel scaled-structural template approach for HT GB calculations, which reduces the computational cost of converging GB structures by a factor of $\sim 3-6$. The grain boundary energies and work of separation are rigorously validated against previous experimental and computational data. Using this large GB dataset, we develop an improved predictive model for the GB energy of different elements based on the cohesive energy and shear modulus. The open GBDB represent a significant step forward in the availability of first principles GB properties, which we believe would help guide the future design of polycrystalline materials.

cond-mat.mtrl-sci↗