HADB: A materials-property database for hard-coating alloys

Levämäki, Henrik; Bock, Florian; Sangiovanni, Davide; Johnson, Lars; Tasnádi, Ferenc; Armiento, Rickard; Abrikosov, Igor A.

doi:10.1016/j.tsf.2022.139627

Cited by 3 publications

(3 citation statements)

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“…A key goal for the OPTIMADE API is for it to act as an enabling technology for materials discovery, design and other new research avenues. Furthermore, there has been additional use of OPTI-MADE in the literature: firstly how OPTIMADE has been a central tool to access materials data for materials discovery, secondly as a template for materials data curation and access, and thirdly through online web-based interfaces: Discovery The hard-coating alloys database (HADB) 74 exploited the OPTIMADE API to rapidly and easily provide the browser-based graphical web interface as well as a RESTful API. A second application of the OPTIMADE API was to query and retrieve an unprecedented volume of data to train an attentionbased crystal graph convolutional neural network to accurately predict the formation energy, total energy, bandgap, and Fermi energy of a broad range of crystals 136 .…”

Section: Application Of Optimade To Real-life Problemsmentioning

confidence: 99%

“…The hard-coating alloys database (HADB) 74 exploited the OPTI-MADE API to rapidly and easily provide the browser-based graphical web interface as well as a RESTful API. A second application of the OPTIMADE API was to query and retrieve an unprecedented volume of data to train an attention-based crystal graph convolutional neural network to accurately predict the formation energy, total energy, bandgap, and Fermi energy of a broad range of crystals.…”

Section: Discoverymentioning

confidence: 99%

See 1 more Smart Citation

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Evans,

Bergsma,

Merkys

et al. 2024

Digital Discovery

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Section: Application Of Optimade To Real-life Problemsmentioning

confidence: 99%

Section: Discoverymentioning

confidence: 99%

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Evans,

Bergsma,

Merkys

et al. 2024

Digital Discovery

Self Cite

View full text Add to dashboard Cite

show abstract

“…We highlight examples which benefit from access to the wealth of data available in large databases (e.g., the hard-coating alloys database discussed immediately below), and examples that benefit from access to specialist data available only in the small and focused databases (e.g., Section 4.1.1). Furthermore, there has been additional use of OPTI-MADE in the literature: firstly how OPTIMADE has been a central tool to access materials data for materials discovery, secondly as a template for materials data curation and access, and thirdly through online web-based interfaces: Discovery The hard-coating alloys database (HADB) 74 exploited the OPTIMADE API to rapidly and easily provide the browser-based graphical web interface as well as a RESTful API. A second application of the OPTIMADE API was to query and retrieve an unprecedented volume of data to train an attentionbased crystal graph convolutional neural network to accurately predict the formation energy, total energy, bandgap, and Fermi energy of a broad range of crystals 136 .…”

Section: Application Of Optimade To Real-life Problemsmentioning

confidence: 99%

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

Evans¹,

Andersen²,

Dwaraknath³

et al. 2021

JOSS

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Active learning with moment tensor potentials to predict material properties: Ti0.5Al0.5N at elevated temperature

Bock,

Tasnádi,

Abrikosov

2024

Journal of Vacuum Science &Amp; Technology A

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Transition metal nitride alloys possess exceptional properties, making them suitable for cutting applications due to their inherent hardness or as protective coatings due to corrosion resistance. However, the computational demands associated with predicting these properties using ab initio methods can often be prohibitively high at the conditions of their operation at cutting tools, that is, at high temperatures and stresses. Machine learning approaches have been introduced into the field of materials modeling to address the challenge. In this paper, we present an active learning workflow to model the properties of our benchmark alloy system cubic B1 Ti0.5Al0.5N at temperatures up to 1500 K. With a minimal requirement of prior knowledge about the alloy system for our workflow, we train a moment tensor potential (MTP) to accurately model the material’s behavior over the entire temperature range and extract elastic and vibrational properties. The outstanding accuracy of MTPs with relatively little training data demonstrates that the presented approach is highly efficient and requires about two orders of magnitude less computational resources than state-of-the-art ab initio molecular dynamics.

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HADB: A materials-property database for hard-coating alloys

Cited by 3 publications

References 35 publications

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

Active learning with moment tensor potentials to predict material properties: Ti0.5Al0.5N at elevated temperature

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