We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys (HEAs). MLIPs enabled accurate high throughput calculations of elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very time consuming calculations when performed using density functional theory (DFT). We demonstrate that the MLIP predicted properties compare well with the DFT results on various test cases and are consistent with the available experimental data. The MLIPs are also utilized for high throughput optimization of non equimolar R4 candidates by guided iterative tuning of R4 compositions to discover candidate materials with promising hardness-ductility combinations. We also used this approach to study the effect of Ti concentration on the elastic and mechanical properties of R4, by statistically averaging the properties of over 100 random structures. MLIP predicted hardness and bulk modulus of equimolar R4 and R5 HEAs are validated using experimentally measured Vickers hardness and modulus. This approach opens a new avenue for employing MLIPs for HEA candidate optimization.
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperform…
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTP) are polynomial-like functions of interatomic distances and angles. The Gaussian Approximation Potential (GAP) framework uses kernel regression, and we use the Smooth Overlap of Atomic Positions (SOAP) representation of atomic neighbourhoods that consists of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbour d…
We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-V-W quinary system and use it to study segregation and defects in the body-centred cubic refractory high-entropy alloy MoNbTaVW. In the bulk alloy, we observe clear ordering of mainly Mo-Ta and V-W binaries at low temperatures. In damaged crystals, our simulations reveal clear segregation of vanadium, the smallest atom in the alloy, to compressed interstitial-rich regions like radiation-induced dislocation loops. Vanadium also dominates the population of single self-interstitial atoms. In contrast, due to its…
In this paper we present new parameters of the TB-SMA interatomic potentials for the Pt/Cu(111) surface alloy. The parameters are fitted using both the experimental and {\it ab initio} data. The potentials reproduce not only the bulk properties of copper and platinum, but also the energy characteristics of the Pt/Cu(111) surface alloy. The potentials can be used for the simulations of the growth of the Pt/Cu(111) surface alloy on the atomic scale.
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our approach significantly reduces the amount of DFT calculations needed, resorting to DFT only to produce the training data, while structural optimization is performed using the interatomic potentials. Our approach is not limited to one (or a small number of) lattice types (as is the case for cluster expansion, for example) and can predict structures with lattice…
Atomistic simulations of liquid alloys face the challenge of correctly modeling basic thermodynamic properties. In this work we present an interatomic potential for the Li-Pb system, as well as a study of physical properties of Li-Pb alloys. Despite the complexity due to Li-Pb being a compound forming system where charge transfer is expected, we show here how the empirical EAM formalism is able to satisfactorily describe several physical properties in a wide range of Li concentration. Application of our potential to Li-Pb eutectic allows us to correctly predict many physical properties observe…
We developed new modified embedded-atom method (MEAM) interatomic potentials for the Mg-Al alloy system using a first-principles method based on density functional theory (DFT). The materials parameters, such as the cohesive energy, equilibrium atomic volume, and bulk modulus, were used to determine the MEAM parameters. Face-centered cubic, hexagonal close packed, and cubic rock salt structures were used as the reference structures for Al, Mg, and MgAl, respectively. The applicability of the new MEAM potentials to atomistic simulations for investigating Mg-Al alloys was demonstrated by perform…
An interatomic potential (termed EAM-21) has been developed with the embedded atomic method (EAM) for CrFeMnNi quaternary HEAs. This potential is based on a previously developed potential for CrFeNi ternary alloys. The parameters to develop the potential were determined by fitting to experimental values, density functional theory (DFT) and thermodynamic calculations, to reproduce the main crystal characteristics, namely: the stability of the fcc phase, elastic constants, and stacking fault energy. Its applicability for the study of plastic deformation mechanisms was checked by calculations of …
An amorphous Se$_{0.80}$S$_{0.20}$ alloy produced by Mechanical Alloying was studied by Raman spectroscopy, x-ray diffraction, extended x-ray absorption fine structure (EXAFS) and optical absorption spectroscopy, and also through reverse Monte Carlo simulations of its total structure factor and EXAFS data. Its vibrational modes, optical gap and structural properties as average interatomic distances and average coordination numbers were determined and compared to those found for an amorphous Se$_{0.90}$S$_{0.10}$ alloy. The results indicate that coordination numbers, interatomic distances and a…
Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed through the embedded atom method (EAM), are derived from semi-empirical considerations and contain unknown parameters that must be fitted based on training data. In the present work, we investigate Bayesian calibration as a means of fitting EAM potentials for binary alloys. The Bayesian setting naturally assimilates probabilistic assertions about uncertain quantities. In this way, uncertainties about model parameters and …
We develop a new approach to determining LJ-EAM potentials for alloys and use these to determine the solid-liquid phase diagrams for binary metallic alloys using Kofke's Gibbs-Duhem integration technique combined with semigrand canonical Monte Carlo simulations. We demonstrate that it is possible to produce a wide-range of experimentally observed binary phase diagrams (with no intermetallic phases) by reference to the atomic sizes and cohesive energies of the two elemental materials. In some cases, it is useful to employ a single adjustable parameter to adjust the phase diagram (we provided a …
We propose a simple scheme to construct composition-dependent interatomic potentials for multicomponent systems that when superposed onto the potentials for the pure elements can reproduce not only the heat of mixing of the solid solution in the entire concentration range but also the energetics of a wider range of configurations including intermetallic phases. We show that an expansion in cluster interactions provides a way to systematically increase the accuracy of the model, and that it is straightforward to generalise this procedure to multicomponent systems. Concentration-dependent intera…
Compositional effects in NaK alloy clusters have been studied using bond order length strength notation and density functional theory calculations. The results reveal binding energy shifts of the NaK alloy clusters under different elemental compositions. Atomic arrangements that can be used to predict the structures of stable experimental NaK alloys were also obtained. Our study of these alloy nanoclusters has uncovered a trend correlating atomic position and composition with binding energy. We believe this data will help in the experimental preparation of alloy nanoclusters.
We screen a large chemical space of perovskite alloys for systems with the right properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phase diagram, a crucial feature for high piezoelectric performance. We start from alloy end-points previously identified in a high-throughput computational search. An interpolation scheme is used to estimate the relative energies between different perovskite distortions for alloy compositions with a minimum of computational effort. Suggested alloys are further screened for thermodynamic stability. The screening identifi…
First-principles-based materials screening is systematically performed to discover new combinations of chemical elements possibly making shape-memory alloys (SMAs). The B2, D03, and L21 crystal structures are considered as the parent phases, and the 2H and 6M structures are considered as the martensitic phases. 3,384 binary and 3,243 ternary alloys (6,627 in total) with stoichiometric composition ratios are investigated by the materials screening in terms of energetic and dynamical stabilities of the martensitic phases as well as structural compatibility between the parent and the martensitic …
A nanocrystalline metal's strength increases significantly as its grain size decreases, a phenomenon known as the Hall-Petch relation. Such relation, however, breaks down when the grains become too small. Experimental studies have circumvented this problem in a set of Ni-Mo alloys by stabilizing the grain boundaries (GB). Here, using atomistic simulations with a machine learning-based interatomic potential (ML-IAP), we demonstrate that the inverse Hall-Petch relation can be correctly reproduced due to a change in the dominant deformation mechanism as the grain becomes small in the Ni-Mo polycr…
Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys
State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所非线性力学国家重点实验室）；Department of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology（代尔夫特理工大学机械工程学院材料科学与工程系）；Faculty of Engineering Technology, University of Twente（特温特大学工程技术学院）；Laboratory for multiscale mechanics and medical science, SV LAB, School of Aerospace, Xi’an Jiaotong University（西安交通大学航天学院SV多尺度力学与医学科学实验室）；College of Physical Science and Technology, Bohai University（渤海大学物理科学与技术学院）
The most critical limitation to the wide-scale use of classical molecular dynamics for alloy design is the availability of suitable interatomic potentials. In this work, we demonstrate a simple procedure to generate a library of accurate binary potentials using already-existing single-element potentials that can be easily combined to form multi-component alloy potentials. For the Al-Ni, Cu-Au, and Cu-Al-Zr systems, we show that this method produces results comparable in accuracy to alloy potentials where all parts have been fitted simultaneously, without the additional computational expense. F…
In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants, misfit volumes, etc.), representative for the macroscopic behavior. The material properties are usually computed using special quasi-random structures (SQSs), in tandem with density functional theory (DFT). However, DFT scales cubically with the number of atoms and is thus impractical for a screening over many alloy compositions. Here, we present a novel methodol…
We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of ~10000 or more training candidate configurations with 50-100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to ~100 most distinct configurations - fo…
We develop a fundamental theory of the long-range electrostatic interactions in two-dimensional crystals by performing a rigorous study of the nonanalyticities of the Coulomb kernel. We find that the dielectric functions are best represented by $2\times 2$ matrices, with nonuniform macroscopic potentials that are two-component hyperbolic functions of the out-of-plane coordinate, $z$. We demonstrate our arguments by deriving the long-range interatomic forces in the adiabatic regime, where we identify a formerly overlooked dipolar coupling involving the out-of-plane components of the dynamical c…
Precipitate phase selection and grain boundary morphology in Cu-Ni-Si-Mn alloys: A machine-learning interatomic potential study
Korea Electrotechnology Research Institute (KERI)（韩国电工技术研究所）；Korea Institute of Materials Science (KIMS)（韩国材料科学研究所）；Seoul National University（首尔大学）；University of Science and Technology (UST)（科学技术联合大学院大学）；Korea Institute for Advanced Study（韩国高等研究院）
Using the self-consistent field approximation, the static concentration waves approach and the Onsager-type kinetics equations, the descriptions of both the statistical thermodynamics and the kinetics of an atomic ordering of D019 phase are developed and applied for h.c.p.-Ti-Al alloy. The model of order-disorder phase transformation describes the phase transformation of h.c.p. solid solution into the D019 phase. Interatomic-interaction parameters are estimated for both approximations: one supposes temperature-independent interatomic-interaction parameters, while the other one includes the tem…
The design of corrosion-resistant high entropy alloys (CR-HEAs) is challenging due to the alloys' virtually astrological composition space. To facilitate this, efficient and reliable high-throughput exploratory approaches are needed. Toward this end, the current work reports a first-principles-based approach exploiting the correlations between work function, surface energy, and corrosion resistance (i.e., work function and surface energy are, by definitions, proportional and inversely proportional to an alloy's inherent corrosion resistance, respectively). Two Bayesian CALPHAD models (or datab…
We have used the locally self-consistent Green's function (LSGF) method in supercell calculations to establish the distribution of the net charges assigned to the atomic spheres of the alloy components in metallic alloys with different compositions and degrees of order. This allows us to determine the Madelung potential energy of a random alloy in the single-site mean field approximation which makes the conventional single-site density-functional- theory coherent potential approximation (SS-DFT-CPA) method practically identical to the supercell LSGF method with a single-site local interaction …
Body-centered cubic (bcc) refractory multicomponent alloys are of great interest due to their remarkable strength at high temperatures. Meanwhile, further optimizing the chemical compositions of these alloys to achieve a combination of high strength and room-temperature ductility remains challenging, which would require systematic predictions of the correlated alloy properties across a vast compositional space. In the present work, we performed first-principles calculations with the special quasi-random structure (SQS) method to predict the unstable stacking fault energy ($γ_{usf}$) of the $(1…
To enable accurate molecular dynamics simulations of iron-chromium alloys with surfaces, we develop, based on density-functional-theory (DFT) calculations, a new interatomic Fe-Cr potential in the Tersoff formalism. Contrary to previous potential models, which have been designed for bulk Fe-Cr, we extend our potential fitting database to include not only conventional bulk properties but also surface-segregation energies of Cr in bcc Fe. In terms of reproducing our DFT results for the bulk properties, the new potential is found to be superior to the previously developed Tersoff potential and co…
The puzzling absence of Pu magnetic moments in a PuAm environment is explored using the self-consistent Dynamical Mean Field Theory (DMFT) calculations in combination with the Local Density Approximation. We argue that delta-Pu -Am alloys provide an ideal test bed for investigating the screening of moments from the single impurity limit to the dense limit. Several important effects can be studied: volume expansion, shift of the bare Pu on-site f energy level, and the reduction of the hybridization cloud resulting from the collective character of the Kondo effect in the Anderson lattice. These …
Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials
Core-level shifts and core-hole screening effects in alloy formation are studied ``ab initio'' by constrained-density-functional total-energy calculations. For our case study, the ordered intermetallic alloy MgAu, final-state effects are essential to account for the experimental Mg 1s shift, while they are negligible for Au 4f. We explain the differences in the screening by analyzing the calculated charge density response to the core hole perturbation.
We combine the shoving model of $T$-dependent viscosity of supercooled liquids with the Zwanzig-Mountain formula for the high-frequency shear modulus, using the $g(r)$ of MD simulations of metal alloys as the input. This scheme leads to a semi-analytical expression for the viscosity as a function of temperature, which provides a three-parameter model fitting of experimental data of viscosity for the same alloy for which $g(r)$ was calculated. The model provides direct access to the influence of atomic-scale physical quantities such as the interatomic potential $ϕ(r)$, on the viscosity and frag…
A quantitative description of the configurational part of the total energy of metallic alloys with substantial atomic size difference cannot be achieved in the atomic sphere approximation: It needs to be corrected at least for the multipole moment interactions in the Madelung part of the one-electron potential and energy. In the case of a random alloy such interactions can be accounted for only by lifting the atomic sphere and single-site approximations, in order to include the polarization due to local environment effects. Nevertheless a simple parameterization of the screened Coulomb interac…
We report the parameter-free, density functional theory calculations of interatomic exchange interactions and Curie temperatures of half-metallic ferrimagnetic full Heusler alloys Mn2VZ (Z=Al, Ge). To calculate the interatomic exchange interactions we employ the frozen-magnon approach. The Curie temperatures are calculated within the mean-field approximation to the classical Heisenberg Hamiltonian by solving a matrix equation for a multi-sublattice system. Our calculations show that, although a large magnetic moment is carried by Mn atoms, competing ferromagnetic (inter sublattice) and antifer…
An exciting development over the past few decades has been the use of high-throughput computational screening as a means of identifying promising candidate materials for a variety of structural or functional properties. Experimentally, it is often found that the highest-performing materials contain substantial atomic site disorder. These are frequently overlooked in high-throughput computational searches however, due to difficulties in dealing with materials that do not possess simple, well-defined crystallographic unit cells. Here we demonstrate that the screening of magnetocaloric materials …
Machine learning interatomic potentials for solid-state precipitation
Laboratory of materials design and simulation (MADES), Institute of Materials, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院材料研究所材料设计与模拟实验室）；National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院国家新材料计算设计与发现中心）
