Data-centric science for materials innovation

Tanaka, Isao; Rajan, Krishna; Wolverton, Christopher

doi:10.1557/mrs.2018.205

Cited by 30 publications

(21 citation statements)

References 45 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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“…In all three the main underlying quantum engine is the VASP code [5], and the data were originally not open to the public, but-as will be described below-they are now open and part of the Novel Materials Discovery (NOMAD) data collection. For an overview of the current status of data-centric science for materials innovation and a more detailed account of the mentioned important initiatives we refer to a special issue of the Materials Research Society Bulletin [6].…”

Section: Introductionmentioning

confidence: 99%

The NOMAD laboratory: from data sharing to artificial intelligence

2019

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The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computational materials science data. It accounts for the various aspects of data being a crucial raw material and most relevant to accelerate materials research and engineering. NOMAD, with the NOMAD Repository, and its code-independent and normalized form, the NOMAD Archive, comprises the worldwide largest data collection of this field. Based on its findable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, the NOMAD Encyclopedia, and artificial-intelligence tools. The latter are realized in the NOMAD Analytics Toolkit. Prerequisite for all this is the NOMAD metadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier to make data citable. Developments and advancements of parsers and metadata are organized jointly with users and code developers. In this work, we review the NOMAD concept and implementation, highlight its orthogonality to and synergistic interplay with other data collections, and provide an outlook regarding ongoing and future developments.

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“…Machine learning (ML) techniques offer an alternative way to create predictive models that bridge the materials property of interest with its potential descriptors quickly and automatically [13][14][15][16] . In addition, the model created from ML does not require to rely on presumed fitting expressions or any historical intuition of material behaviors.…”

Section: Introductionmentioning

confidence: 99%

Predicting densities and elastic moduli of SiO2-based glasses by machine learning

Zhao

Zhang

et al. 2020

npj Comput Mater

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Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales.Here we show that the densities and elastic moduli of SiO2-based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (>10) types of additive oxides besides SiO2. Our machine learning approach relies on a training set generated by high-throughput molecular dynamic (MD) simulations, a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding, and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine (GBM-LASSO). The predictions of the ML model are comprehensively compared and validated with a large amount of both simulation and experimental data. By just training with a dataset only composed of binary and ternary glass samples, our model shows very promising capabilities to predict the density and elastic moduli for k-nary SiO2based glasses beyond the training set. As an example of its potential applications, our GBM-LASSO model was used to perform a rapid and low-cost screening of many (~10 5 ) compositions of a multicomponent glass system to construct a compositionalproperty database that allows for a fruitful overview on the glass density and elastic properties.

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Data-centric science for materials innovation

Abstract: Abstract

Cited by 30 publications

References 45 publications

The evolving landscape for alloy design

The evolving landscape for alloy design

The NOMAD laboratory: from data sharing to artificial intelligence

Predicting densities and elastic moduli of SiO2-based glasses by machine learning

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