Unveiling the thermodynamic driving forces for high entropy alloys formation through big data ab initio analysis

Bokas, G.B.; Chen, Wei; Hilhorst, Antoine; Jacques, Pascal; Gorsse, Stéphane; Hautier, Geoffroy

doi:10.1016/j.scriptamat.2021.114000

Cited by 19 publications

(11 citation statements)

References 51 publications

(55 reference statements)

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“…Bokas et al used DFT free energies of binary solid-solutions to fit a regular solution parameter Ω for each binary pair of elements out of 27 elements used in HEAs [32]. These regular solution parameters were used to calculate solid-solution free energies in our inverse hull webs.…”

Section: Methodology Calculation Of Compound Free Energiesmentioning

confidence: 99%

“…Here, we calculate the formation enthalpies of solid-solution phases using DFT-calculated regular solution parameters from Bokas et al [32], and use the formation enthalpies of ordered intermetallics through the Materials Project [33]. We assume that differences in vibrational entropy per atom between solid phases is negligible at high temperatures, as can be expected from the law of Dulong-Petit [34].…”

Section: Application Of Inverse Hull Webs To Heasmentioning

confidence: 99%

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Visualizing temperature-dependent phase stability in high entropy alloys

et al. 2021

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High entropy alloys (HEAs) contain near equimolar amounts of five or more elements and are a compelling space for materials design. In the design of HEAs, great emphasis is placed on identifying thermodynamic conditions for single-phase and multi-phase stability regions, but this process is hindered by the difficulty of navigating stability relationships in high-component spaces. Traditional phase diagrams use barycentric coordinates to represent composition axes, which require (N – 1) spatial dimensions to represent an N-component system, meaning that HEA systems with N > 4 components cannot be readily visualized. Here, we propose forgoing barycentric composition axes in favor of two energy axes: a formation-energy axis and a ‘reaction energy’ axis. These Inverse Hull Webs offer an information-dense 2D representation that successfully captures complex phase stability relationships in N ≥ 5 component systems. We use our proposed diagrams to visualize the transition of HEA solid-solutions from high-temperature stability to metastability upon quenching, and identify important thermodynamic features that are correlated with the persistence or decomposition of metastable HEAs.

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Section: Methodology Calculation Of Compound Free Energiesmentioning

confidence: 99%

Section: Application Of Inverse Hull Webs To Heasmentioning

confidence: 99%

Visualizing temperature-dependent phase stability in high entropy alloys

et al. 2021

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“…Also, in recent years, high entropy alloys (HEAs) [55] have been proposed for many applications due to their promising properties, including the resistance to irradiation. In the specific case of HEAs, high-throughput computations and active learning methods can be combined to predict new stable compositions with tailored properties [56][57][58], which are methods that heavily rely on the availability of HPC. In any case, given the timelines of the ITER and IFMIF-DONES (International Fusion Materials Irradiation Facility DEMO Oriented Neutron Source) experiments, research on fusion materials will be definitely guided by theoretical calculations using multiscale and exascale-oriented methods that will accelerate the future arrival of commercial fusion reactors.…”

Section: Computational Materials Modelling At the Nanoscalementioning

confidence: 99%

Ab initio guided atomistic modelling of nanomaterials on exascale high-performance computing platforms

Gutiérrez Moreno

2024

Nano Futures

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The continuous development of increasingly powerful supercomputers makes theory-guided discoveries in materials and molecular sciences more achievable than ever before. On this ground, the incoming arrival of exascale supercomputers (running over 10^18 floating point operations per second) is a key milestone that will tremendously increase the capabilities of high-performance computing (HPC). The deployment of these massive platforms will enable continuous improvements in the accuracy and scalability of ab initio codes for materials simulation. Moreover, the recent progress in advanced experimental synthesis and characterisation methods with atomic precision has led ab initio-based materials modelling and experimental methods to a convergence in terms of system sizes. This makes it possible to mimic full-scale systems in silico almost without the requirement of experimental inputs. This article provides a perspective on how computational materials science will be further empowered by the recent arrival of exascale HPC, going alongside a mini-review on the state-of-the-art of HPC-aided materials research. Possible challenges related to the efficient use of increasingly larger and heterogeneous platforms are commented on, highlighting the importance of the co-design cycle. Also, some illustrative examples of materials for target applications, which could be investigated in detail in the coming years based on a rational nanoscale design in a bottom-up fashion, are summarised.

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“…First-principles-based electronic structure methods, with remarkable accuracy and sophistication, are the most popular among computational materials scientists [2]. However, most recent breakthroughs in materials for batteries, solar cells and electronic devices were reported in alloys, often with more than 1 additive element [3][4][5][6][7][8][9]. Modeling the properties of these multicomponent alloys involve sampling a vast configurational space which expands exponentially as the number of alloying elements increase.…”

Section: Introductionmentioning

confidence: 99%

Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys

Thekkepat,

Das,

Prosad Dogra

et al. 2023

J. Phys.: Condens. Matter

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Multicomponent alloys are gaining significance as drivers of technological breakthroughs especially in structural and energy storage materials. The vast configuration space of these materials prohibit computational modeling using first-principles based methods alone. The cluster expansion (CE) method is the most widely used tool for modeling configurational disorder in alloys. CE relies on machine learning algorithms to train Hamiltonians and uses first-principles calculated data as training sets. In this paper we present a new compressive sensing-based algorithm for the efficient construction of cluster expansion Hamiltonians of multicomponent alloys. Our algorithm constructs highly sparse and physically reasonable models from a carefully selected small training set of alloy structures. Compared to conventional fitting algorithms, the algorithm achieves more than 50 % reduction in the training set size. The resultant sparse models can sample the configuration space at least 3x faster. We demonstrate this algorithm on 4 different alloy systems, namely Ag-Au, Ag-Au-Cu, Ag-Au-Cu-Pd and (Ge,Sn)(S,Se,Te).The sparse cluster expansion models for these alloys can rapidly reproduce known ground state orderings and order-disorder transitions. Our method can truly enable high-throughput multicomponent alloy thermodynamics by reducing the cost associated with model construction and configuration sampling.

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Unveiling the thermodynamic driving forces for high entropy alloys formation through big data ab initio analysis

Cited by 19 publications

References 51 publications

Visualizing temperature-dependent phase stability in high entropy alloys

Visualizing temperature-dependent phase stability in high entropy alloys

Ab initio guided atomistic modelling of nanomaterials on exascale high-performance computing platforms

Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys

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