Thermodynamic limit for synthesis of metastable inorganic materials

Aykol, Muratahan; Dwaraknath, Shyam; Sun, Wenhao; Persson, Kristin A.

doi:10.1126/sciadv.aaq0148

Cited by 300 publications

(306 citation statements)

References 66 publications

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“…This structural diversity is reflected in many known and hypothetical crystalline structures, [34,143] metastable over an unusually wide stability window, [144] and in a plethora of disordered phases with very diverse properties. This structural diversity is reflected in many known and hypothetical crystalline structures, [34,143] metastable over an unusually wide stability window, [144] and in a plethora of disordered phases with very diverse properties.…”

Section: Carbon Nanomaterialsmentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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Section: Carbon Nanomaterialsmentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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“…In addition, numerous decomposition pathways have to be considered for a comprehensive analysis of the kinetic stability of the material. Despite recent advances in the computational prediction of decomposition pathways and associated nucleation barriers, experimental approaches are still required to reliably probe the synthesizability and stability of novel metastable materials . In recent years, high‐throughput combinatorial materials science methodology has gained tremendous interest.…”

Section: Accessing Metastable Phase Spacementioning

confidence: 99%

“…Despite recent advances in the computational prediction of decomposition pathways [25] and associated nucleation barriers, [26] experimental approaches are still required to reliably probe the synthesizability and stability of novel metastable materials. [8] In recent years, high-throughput combinatorial materials science methodology has gained tremendous interest. Combinatorial deposition followed by spatially resolved, automated characterization offers the promise of rapid and efficient materials screening, optimization, and discovery.…”

Section: Synthesis Routesmentioning

confidence: 99%

“…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 near‐equilibrium materials and incorporating metastable phase space into materials design and discovery efforts …”

Section: Introductionmentioning

confidence: 99%

“…This motivates moving beyond the traditionally explored nearequilibrium materials and incorporating metastable phase space into materials design and discovery efforts. [8][9][10][11][12][13] Alloying, historically, is an extremely successful approach to tailor materials properties and sets the foundation for many materials design efforts. Alloying of compounds with the same crystal structure is routinely employed for tuning functionality, often with the goal of combining favorable properties of the endmember compounds.…”

Section: Introductionmentioning

confidence: 99%

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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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Establishing a Theoretical Landscape for Identifying Basal Plane Active 2D Metal Borides (MBenes) toward Nitrogen Electroreduction

Guo

Lin

et al. 2020

Adv Funct Materials

130

114

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To achieve efficient ammonia synthesis via electrochemical nitrogen reduction reaction (NRR), a qualified catalyst should have both high specific activity and large active surface area. However, integrating these two merits into one single material remains a big challenge due to the difficulty in balancing multiple reaction intermediates. Here, it is demonstrated that the boron‐analogues of MXenes, namely “MBenes”, could cope with the challenge and achieve the high activity and large reaction region simultaneously toward NRR. Using extensive density functional theory computations and taking 16 MBenes as representatives, it is identified that seven MBenes (CrB, MoB, WB, Mo2B, V3B4, CrMnB2, and CrFeB2) not only have intrinsic basal plane activity for NRR with limiting potentials ranging from −0.22 to −0.82 V, but also possess superior capability of suppressing the competitive hydrogen evolution reaction. Particularly, different from the MXenes whose surface oxidation may block the active sites, once oxidized, these MBenes can catalyze NRR via the self‐activating process, reducing O*/OH* into H2O* under reaction conditions, and favoring the N2 electroreduction. As a result, the exceptional activity and selectivity, high active area (≈1019 m−2), and antioxidation nature render these MBenes as pH‐universal catalysts for NH3 production without introducing any dopants and defects.

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Thermodynamic limit for synthesis of metastable inorganic materials

Abstract: Amorphous forms serve as thermodynamic upper bounds on the free energy scale for synthesis of metastable crystalline polymorphs.

Cited by 300 publications

References 66 publications

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Accessing Metastability in Heterostructural Semiconductor Alloys

Establishing a Theoretical Landscape for Identifying Basal Plane Active 2D Metal Borides (MBenes) toward Nitrogen Electroreduction

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