New Wang-Landau approach to obtain phase diagrams for multicomponent alloys

Takeuchi, Kazunari; Tanaka, Ryôhei; Yuge, Koretaka

doi:10.1103/physrevb.96.144202

Cited by 9 publications

(9 citation statements)

References 39 publications

(40 reference statements)

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“…4 and list the resulting T c in Table III. It turns out that our DFT model, together with the numerically estimated S conf , is able to reproduce the experimental trend T c (x = 0.25) ≈ T c (x = 0.5) > T c (x = 0.75), which seems to have been previously possible only by cluster-expansion [26] or effective-medium theory methods [28].…”

Section: Resultssupporting

confidence: 53%

“…Experimental and theoretical studies of SRO, especially in the case of binary alloys, have a long history, and one of the earliest studies of SRO focused on the Cu-Au system [21], which is probably the most widely studied binary system in alloy theory [22,23]. Short-range order and its effect on the * levamaki@kth.se order-disorder transition temperature has mostly been studied using the cluster-expansion method [24][25][26], cluster-variation method [27], or effective-medium theory [28]. Studies based on direct DFT calculations are much more scarce because compared to, e.g., the cluster-expansion method they are computationally much more demanding and there needs to be good approximations for the different free-energy components of each calculated structure.…”

Section: Introductionmentioning

confidence: 99%

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Density functional theory description of random Cu-Au alloys

et al. 2019

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Density functional alloy theory is used to accurately describe the three core effects controlling the thermodynamics of random Cu-Au alloys. These three core effects are exchange correlation (XC), local lattice relaxations (LLRs), and short-range order (SRO). Within the real-space grid-based projector augmented-wave (GPAW) method based on density functional theory (DFT), we adopt the quasinonuniform XC approximation (QNA), and take into account the LLR and the SRO effects. Our approach allows us to study the importance of all three core effects in a unified way within one DFT code. The results demonstrate the importance of the LLR term and show that going from the classical gradient level approximations to QNA leads to accurate formation energies at various degrees of ordering. The order-disorder transition temperatures for the 25%, 50%, and 75% alloys reach quantitative agreement with the experimental values only when also the SRO effects are considered.

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Section: Resultssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Density functional theory description of random Cu-Au alloys

et al. 2019

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“…69,71 It should be noted that the random-walking simulation process in WLMC is not influenced by the phase transition, so the prediction near phase transition temperatures maintains the same accuracy, in contrast to the Metropolis MC, which suffers significant accuracy loss. 71…”

Section: Journal Ofmentioning

confidence: 99%

Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials

Xie,

Zhou,

Jin

et al. 2024

J. Chem. Theory Comput.

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First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonian models with DFT accuracy for efficient simulation of alloyed systems with configurational disorder. Recently, the machine learning force field (MLFF) method has been proposed to tackle complicated multicomponent disordered systems. However, the importance of integrating significant physical considerations, including, in particular, convex hull preservation, which is the prerequisite for the accurate prediction of phase diagrams, into the training process of the MLFF remains rarely addressed. In this work, a workflow is proposed to train a convex-hull-preserved (CHP) MLFF for binary alloy systems, based on which the order−disorder phase boundary is predicted by using the Wang−Landau Monte Carlo (WLMC) technique. The predicted values for order−disorder phase transition temperatures agree well with the experiment. The CHP-MLFF is further used to build CE models with the same accuracy as the MLFF and higher efficiency in sampling configurational space. Using the results obtained from the MLFF-based WLMC simulation as a reference, the performances of different schemes for constructing CE models were evaluated in a transparent manner, which revealed the close correlation between the prediction accuracy of ground-state configurations and that of the order− disorder phase transition temperature. This work clearly indicates the great importance of reproducing the convex hull and energetics of ground-state configurations when constructing surrogate Hamiltonians for the statistical modeling of alloyed systems.

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“…Observed discontinuities in stable compositions suggested the presence of a Cu 3 Au − CuAu two-phase region for temperatures below 700 K. Estimated bounds were determined from the maximum difference in composition between ∆µ values separated by 0.024 eV, restricted to the composition range 0.2 < %Au < 0.6. Based of previous work of Takeuchi et al 17 , bounds for order-disordered two-phase regions were estimated by locating the temperature with maximal heat capacity T C for each constant value of ∆µ and approximating the bounds of the two-phase regions as the compositions at (T C , ∆µ − δ) and (T C , ∆µ + δ) with δ = 0.012 eV (See Fig. S9b).…”

Section: Predicting Phase Stabilitymentioning

confidence: 99%

“…Variance-constrained semi-grand canonical simulations rely on a new thermodynamic ensemble that can be leveraged to compute the free energies of systems within two-phase regions and improve the accuracy of recovered phase boundaries 16 . Furthermore, Wang-Landau methods 11 have been adapted to the materials domain and applied to characterize benchmark systems 17 .…”

Section: Introductionmentioning

confidence: 99%

Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning

Damewood¹,

Schwalbe-Koda²,

Gómez‐Bombarelli³

2021

Preprint

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Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many stateof-the-art disciplines such as the design of high entropy alloys or multicomponent catalysts. To address this issue, we adapt sampling tools built upon machine-learning based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide-ranges of thermodynamic conditions and can be implemented with any internal energy model U , allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

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New Wang-Landau approach to obtain phase diagrams for multicomponent alloys

Cited by 9 publications

References 39 publications

Density functional theory description of random Cu-Au alloys

Density functional theory description of random Cu-Au alloys

Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials

Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning

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