Experimental and molecular dynamics simulation study of structure of liquid and amorphous Ni62Nb38 alloy

Revealing the hidden structure of disordered materials by parameterizing their local structural manifold

Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

A Molecular Dynamics Study on the Dislocation-Precipitate Interaction in a Nickel Based Superalloy during the Tensile Deformation

In the present paper, the dislocation-precipitate interaction in the Inconel 718 superalloy is studied by means of molecular dynamics simulation. The atomistic model composed of the ellipsoidal Ni3Nb precipitate (γ″ phase) and the Ni matrix is constructed, and tensile tests on the composite Ni3Nb@Ni system along different loading directions are simulated. The dislocation propagation behaviors in the precipitate interior and at the surface of the precipitate are characterized. The results indicate that the dislocation shearing and bypassing simultaneously occur during plastic deformation. The contact position of the dislocation on the surface of the precipitate could affect the penetration depth of the dislocation. The maximum obstacle size, allowing for the dislocation shearing on the slip planes, is found to be close to 20 nm. The investigation of anisotropic plastic deformation behavior shows that the composite system under the loading direction along the major axis of the precipitate experiences stronger shear strain localizations than that with the loading direction along the minor axis of the precipitate. The precipitate size effect is quantified, indicating that the larger the precipitate, the lower the elastic limit of the flow stress of the composite system. The dislocation accumulations in the precipitate are also examined with the dislocation densities given on specific slip systems. These findings provide atomistic insights into the mechanical behavior of nickel-based superalloys with nano-precipitates.

Atomic structure of intermetallic compound Nb5Si3by new cluster transformation analysis method

The structure of Nb₅Si₃at the atomic level is fundamental for identifying its complicated structure in atomic simulations and for further understanding the phase selection behaviors during the solidification of Nb-Si alloys. In this study, the structure of Nb₅Si₃was investigated using deep-learning molecular dynamic simulations. The idealβNb₅Si₃is characterized by Nb-centered Voronoi polyhedrons (VPs) <0,0,12,3>, <0,0,12,2>, and Si-centered VPs <0,2,8,2>, <0,2,8,0>. Most initial VPs are distorted at high temperatures due to intense thermal perturbation. A new cluster transformation analysis (CTA) method was proposed to evaluate the stability of ideal VPs against perturbation and predict the possible transformations of the initial VPs in atomic simulations. Most transformations of the initial VPs inβNb₅Si₃originate from distortions at the edges of the Nb-centered VPs and the faces/vertices of the Si-centered VPs. The distorted VPs inβNb₅Si₃at high temperatures are dominated by <0,1,10,4>, <0,1,10,5>, <0,2,8,1> and <1,2,5,3> VPs, which are predicted as the primary transformations by the CTA.

Icosahedral cluster formation in Ni-based hydrogen separation amorphous membranes and the effect of hydrogenation-a first principles structural study

The demand for hydrogen is increasing due to commercialization of fuel cells. Palladium (Pd)-based crystalline membranes have been used for separation of hydrogen from a mixture of gases in coal-based power generation process. However, very high cost of Pd has prompted to explore inexpensive alternative alloys. Amorphous Ni-Nb-Zr alloy membranes are promising cheaper alternatives which exhibit comparable hydrogen permeability to Pd membranes at nominal temperature of ~ 400 °C. Constant exposure to high temperature and hydrogen pressure may lead to changes in the local atomic structure and possible devitrification of membrane. It is critical to understand short-range order of these membranes in order to improve their hydrogen permeability and durability. Icosahedral clusters are the building blocks of amorphous material and hydrogen is expected to interact with them in various different ways. The density functional theory-based molecular dynamics (DFT-MD) approach is the best suited approach to study the local atomic structures for (Ni_0.6Nb_0.4)₉₀Zr₁₀ and (Ni_0.6Nb_0.4)₇₀Zr₃₀ amorphous membranes with the help of nearest neighbor distances and icosahedral cluster analysis. It can help predict the behavior of the membrane under extreme operating conditions. Three types of icosahedra (so called Ni-centered, Zr-centered, and Nb-centered) were identified in six different compositions in these amorphous alloys. Evolution of these icosahedra with temperature and in the presence of hydrogen gave an insight into the local structure of the membrane. Zr plays an important role in the formation of icosahedra. Hydrogen atoms interact with the icosahedra in three different ways. It is observed that H atoms did not show tendency to enter Ni-centered icosahedra leading to easier hydrogen diffusion outside the icosahedra. Hence, the more the number of Ni-centered icosahedra, the better the permeation properties of the alloy.

Learning grain boundary segregation energy spectra in polycrystals

The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.

Predicting segregation energies of alloy systems can be challenging even for a single grain boundary. Here the authors propose a machine-learning framework, which maps the local environments on a distribution of segregation energies, to predict segregation energies of alloy elements in polycrystalline materials.

Curie-Weiss behavior of liquid structure and ideal glass state

We present the results of a structural study of metallic alloy liquids from high temperature through the glass transition. We use high energy X-ray scattering and electro-static levitation in combination with molecular dynamics simulation and show that the height of the first peak of the structure function, S(Q) - 1, follows the Curie-Weiss law. The structural coherence length is proportional to the height of the first peak, and we suggest that its increase with cooling may be related to the rapid increase in viscosity. The Curie temperature is negative, implying an analogy with spin-glass. The Curie-Weiss behavior provides a pathway to an ideal glass state, a state with long-range correlation without lattice periodicity, which is characterized by highly diverse local structures, reminiscent of spin-glass.

A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic heterogeneity remain elusive. Here, we demonstrate that novel site environment features that characterize interstice distributions around atoms combined with machine learning (ML) can reliably identify plastic sites in several Cu-Zr compositions. Using only quenched structural information as input, the ML-based plastic probability estimates ("quench-in softness" metric) can identify plastic sites that could activate at high strains, losing predictive power only upon the formation of shear bands. Moreover, we reveal that a quench-in softness model trained on a single composition and quench rate substantially improves upon previous models in generalizing to different compositions and completely different MG systems (Ni₆₂Nb₃₈, Al₉₀Sm₁₀ and Fe₈₀P₂₀). Our work presents a general, data-centric framework that could potentially be used to address the structural origin of any site-specific property in MGs.

Universal nature of dynamic heterogeneity in glass-forming liquids: A comparative study of metallic and polymeric glass-forming liquids

Glass-formation is a ubiquitous phenomenon that is often observed in a broad class of materials ranging from biological matter to commonly encountered synthetic polymer, as well as metallic and inorganic glass-forming (GF) materials. Despite the many regularities in the dynamical properties of GF materials, the structural origin of the universal dynamical properties of these materials has not yet been identified. Recent simulations of coarse-grained polymeric GF liquids have indicated the coexistence of clusters of mobile and immobile particles that appear to be directly linked, respectively, to the rate of molecular diffusion and structural relaxation. The present work examines the extent to which these distinct types of "dynamic heterogeneity" (DH) arise in metallic GF liquids (Cu-Zr, Ni-Nb, and Pd-Si alloys) having a vastly different molecular structure and chemistry. We first identified mobile and immobile particles and their transient clusters and found the DH in the metallic alloys to be remarkably similar in form to polymeric GF liquids, confirming the "universality" of the DH phenomenon. Furthermore, the lifetime of the mobile particle clusters was found to be directly related to the rate of diffusion in these materials, while the lifetime of immobile particles was found to be proportional to the structural relaxation time, providing some insight into the origin of decoupling in GF liquids. An examination of particles having a locally preferred atomic packing, and clusters of such particles, suggests that there is no one-to-one relation between these populations of particles so that an understanding of the origin of DH in terms of static fluid structure remains elusive.

Effects of Si solute on the glass formation and atomic structure of Pd liquid

Molecular dynamics simulations were performed to study the effects of Si solute on the glass formation and crystallization of Pd liquid. Pure Pd samples prepared by a quenching process with a cooling rate of 10¹³ K s^-1 can be in an amorphous state but the structural analysis indicates there is nearly no glass-forming motif in the sample. However, doping a small amount of Si (Si concentration ~4%) the sample can be vitrified at a cooling rate of 10¹² K s^-1. The glass-forming motifs such as Pd-centered Z13, Si-centered Z9-like and mixed-ICO-cube clusters with five-fold local symmetry are found to be the dominant short-range orders in the glassy samples. With the increasing of the Si-doping concentration, these glass-forming motifs tend to aggregate and connect with each other forming a network structure. Our calculated results revealed that Si solutes in liquid Pd can significantly enhance the glass-forming ability.

Crystal genes in a marginal glass-forming system of Ni50Zr50

The marginal glass-forming ability (GFA) of a binary Ni-Zr system is an issue to be explained considering numerous bulk metallic glasses (BMGs) found in a Cu-Zr system. Using molecular dynamics, the structures and dynamics of Ni₅₀Zr₅₀ metallic liquid and glass are investigated at the atomistic level. To achieve a well-relaxed glassy sample, a sub-T_g annealing method is applied and the final sample is closer to the experiments than the models prepared by continuous cooling. With the state-of-the-art structural analysis tools such as cluster alignment and pair-wise alignment methods, two glass-forming motifs with some mixed traits of a metastable B2 crystalline phase and a crystalline Ni-centered B33 motif are found to be dominant in the undercooled liquid and glass samples. A new chemical order characterization on each short-range order (SRO) structure is accomplished based on the cluster alignment method. The significant amount of the crystalline motif and the few icosahedra in the glassy sample deteriorate the GFA.