Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

Oses, Corey; Toher, Cormac; Curtarolo, Stefano

doi:10.1557/mrs.2018.207

Cited by 43 publications

(33 citation statements)

References 89 publications

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“…Efforts that seek to do this systematically include the Materials Project, the Open Quantum Materials Database (OQMD), Automatic Flow for Materials Discovery (AFLOW), and the Novel Materials Discovery Laboratory (NOMAD), which are large interactive repositories of materials properties that have been calculated with density functional theory. [13][14][15][16] Unfortunately, the availability of powerful first-principles software packages is only a first step in developing comprehensive thermodynamic descriptions in new composition spaces. Many alloys are designed for high-temperature applications or exploit properties of solid Figure 2.…”

Section: Thermodynamic Prerequisitesmentioning

confidence: 99%

The evolving landscape for alloy design

Pollock

Ven

2019

MRS Bull.

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Section: Thermodynamic Prerequisitesmentioning

confidence: 99%

The evolving landscape for alloy design

Pollock

Ven

2019

MRS Bull.

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“…Through this work, we aim to accelerate materials innovation by developing a rapid predictor of the stability of high-entropy materials and demonstrating the model's capability to predict single-or multi-phase results. With regard to speed, our ML model can evaluate the EFA of a single composition in under a millisecond, compared with hundreds of hours per composition with DFT, even using efficient automatic frameworks such as Automatic-Flow (AFLOW) 49 . The robustness of the model is investigated by focusing on locating successful five component compositions containing all three of the Group VI metals (Cr, Mo, and W) as 60% of the cation sublattice.…”

Section: Introductionmentioning

confidence: 99%

Discovery of high-entropy ceramics via machine learning

et al. 2020

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Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach's suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a highthroughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance.

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“…Technological developments in rapidly evolving fields like optoelectronics or renewable energy generation call for new materials with tailored multi‐property functionality. Fueled by significant advances in high‐throughput computation and combinatorial materials science, the design and discovery of novel inorganic materials have evolved into vibrant fields of research . Desired functionality can often be found above the convex hull, that is, in materials which are metastable with respect to the thermodynamic equilibrium state of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Fueled by significant advances in high-throughput computation and combinatorial materials science, the design and discovery of novel inorganic materials have evolved into vibrant fields of research. [1][2][3][4][5][6][7] Desired functionality can often be found above the convex hull, that is, in materials which are metastable with respect to the thermodynamic equilibrium state of the system. This motivates moving beyond the traditionally explored nearequilibrium materials and incorporating metastable phase space into materials design and discovery efforts.…”

Section: Introductionmentioning

confidence: 99%

Accessing Metastability in Heterostructural Semiconductor Alloys

Siol

2019

Physica Status Solidi (a)

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The increasing demand for novel inorganic materials with targeted functionality motivates moving beyond the area of traditionally explored near‐equilibrium materials by incorporating metastable phase space into materials design and discovery. It has recently been shown that heterostructural semiconductor alloys can exhibit increased regions of metastable phase space as compared to their isostructural counterparts. This phase space is accessible using non‐equilibrium deposition techniques, providing ample opportunities for the synthesis of novel, metastable, single‐phase alloys. In addition, the composition‐dependent structural transitions in heterostructural systems provide additional degrees of freedom for the tuning of functional properties. This perspective gives insight into the design of heterostructural semiconductor alloys and highlights their potential for future materials discovery. It is shown how combinatorial, non‐equilibrium physical vapor deposition can be used to effectively screen and access previously uncharted metastable phase space in such systems. In addition, it is demonstrated how differences in mixing enthalpies for different structures can be utilized to stabilize new alloy polymorphs with useful properties that cannot be found in either of the alloying endmembers. Finally, it is discussed how this conceptual approach can be used to screen high‐throughput computational databases, potentially revealing numerous materials systems where such novel metastable polymorphs can be discovered.

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Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

Abstract: Abstract

Cited by 43 publications

References 89 publications

The evolving landscape for alloy design

The evolving landscape for alloy design

Discovery of high-entropy ceramics via machine learning

Accessing Metastability in Heterostructural Semiconductor Alloys

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