Conceptual Inorganic Materials Discovery – A Road Map

Jansen, Martin

doi:10.1002/adma.201500143

Cited by 42 publications

(27 citation statements)

References 99 publications

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“…Recent high-throughput and data-driven explorations of materials syntheses have focused on optimizing a particular material system of interest 8 , 9 . Yet, the general landscape of synthesizable materials 10 , 11 spanning across material systems remains largely unexplored. To further encourage rapid and open synthesis discovery in the materials science community, we present here a dataset which collates key synthesis parameters aggregated by chemical composition (e.g., BiFeO 3 ) across 30 commonly-reported oxide systems.…”

Section: Background and Summarymentioning

confidence: 99%

Machine-learned and codified synthesis parameters of oxide materials

et al. 2017

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Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.

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Section: Background and Summarymentioning

confidence: 99%

Machine-learned and codified synthesis parameters of oxide materials

et al. 2017

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“…However, neither chemical intuition nor data-mining techniques are typically useful for these purposes because they have been developed based upon information gathered at atmospheric conditions. Fortunately, the spectacular advances in computer hardware, coupled with the developments in a priori crystal structure prediction (CSP) using Density Functional Theory (DFT) [18][19][20][21][22][23][24] has led to synergy between experiment and theory in high pressure research. 15,[25][26][27][28] Computations are carried out to predict the structures and properties of targets for synthesis, and also to aid in the characterization of phases that have been made in experiment.…”

Section: Introductionmentioning

confidence: 99%

The Search for Superconductivity in High Pressure Hydrides

Zarifi

Terpstra

et al. 2019

Reference Module in Chemistry, Molecular Sciences and Chemical Engineering

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The computational and experimental exploration of the phase diagrams of binary hydrides under high pressure has uncovered phases with novel stoichiometries and structures, some which are superconducting at quite high temperatures. Herein we review the plethora of studies that have been undertaken in the last decade on the main group and transition metal hydrides, as well as a few of the rare earth hydrides at pressures attainable in diamond anvil cells. The aggregate of data shows that the propensity for superconductivity is dependent upon the species used to "dope" hydrogen, with some of the highest values obtained for elements that belong to the alkaline and rare earth, or the pnictogen and chalcogen families.

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“…The most fundamental one is that of individual atoms, and of the 3D structures that they form, driven by physical laws and chemical bonding. [3][4][5] [2] Accurately describing, understanding, and ultimately controlling the atomic-scale structure of matter is therefore a central goal of materials science.…”

Section: Introductionmentioning

confidence: 99%

“…[2] Accurately describing, understanding, and ultimately controlling the atomic-scale structure of matter is therefore a central goal of materials science. [3][4][5]…”

Section: Introductionmentioning

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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Conceptual Inorganic Materials Discovery – A Road Map

Cited by 42 publications

References 99 publications

Machine-learned and codified synthesis parameters of oxide materials

Machine-learned and codified synthesis parameters of oxide materials

The Search for Superconductivity in High Pressure Hydrides

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

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