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HADB: A materials-property database for hard-coating alloys
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Physics. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Physics. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Physics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1379-6656
Sandvik Coromant, Sweden.
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2023 (English)In: Thin Solid Films, ISSN 0040-6090, E-ISSN 1879-2731, Vol. 766, article id 139627Article in journal (Refereed) Published
Abstract [en]

Data-driven approaches are becoming increasingly valuable for modern science, and they are making their way into industrial research and development (R&D). Supervised machine learning of statistical models can utilize databases of materials parameters to speed up the exploration of candidate materials for experimental synthesis and characterization. In this paper we introduce the HADB database, which contains properties of industrially relevant chemically disordered hard-coating alloys, focusing on their thermodynamic, elastic and mechanical properties. We present the technical implementations of the database infrastructure including support for browse, query, retrieval, and API access through the OPTIMADE API to make this data findable, accessible, interoperable, and reusable (FAIR). Finally, we demonstrate the usefulness of the database by training a graph -based machine learning (ML) model to predict elastic properties of hard-coating alloys. The ML model is shown to predict bulk and shear moduli for out out-of-sample alloys with less than 6 GPa mean average error.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE SA , 2023. Vol. 766, article id 139627
Keywords [en]
Database; Chemically disordered alloys; Hard coatings; Machine learning; Elastic properties
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:liu:diva-191645DOI: 10.1016/j.tsf.2022.139627ISI: 000914738600001OAI: oai:DiVA.org:liu-191645DiVA, id: diva2:1734874
Note

Funding Agencies|Competence Center Functional Nanoscale Materials (FunMat-II) , Sweden (Vinnova) [2016-05156]; Knut and Alice Wallenberg Foundation, Sweden (Wallenberg Scholar Grant) [KAW-2018.0194]; Swedish Government [2020-05402]; Swedish e-Science Research Centre (SeRC) , Sweden; Swedish Research Council (VR) , Sweden [VR-2021-04426]; VR, Sweden [2018-05973]; Swedish Research Council, Sweden; [2009 00971]

Available from: 2023-02-07 Created: 2023-02-07 Last updated: 2024-04-02
In thesis
1. Combining ab‐initio and machine learning techniques for theoretical simulations of hard nitrides at extreme conditions
Open this publication in new window or tab >>Combining ab‐initio and machine learning techniques for theoretical simulations of hard nitrides at extreme conditions
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis I focus on combining the high accuracy of first-principles calculations with modern machine learning methods to make large scale investigations of industrially relevant nitride systems reliable and computationally viable. I study the electronic, thermodynamic and mechanical properties of two families of compounds: Ti1−xAlxN alloys at the operational conditions of industrial cutting tools and ReNx systems at crushing pres-sures comparable to inner earth core conditions. Standard first-principles simulations of materials are usually carried out at zero temperature and pressure, and while many state-of-the-art approaches can take these effects into account, they are usually accompanied by a substantial increase in computational demand. In this thesis I therefore explore the possiblities of studying materials at extreme conditions using machine learning methods with extraordinary efficiency without loss of calculational accuracy. 

Ti1−xAlxN alloy coatings exhibit exceptional properties due to their inherent ability to spinodally decompose at elevated temperature, leading to age-hardening. Since the cubic B1 phase of Ti1−xAlxN is well-studied, available high-accuracy first-principles data served as both a benchmark and data set on which to train a machine learning interatomic potential. Using the reliable moment tensor potentials, an investigation of the accuracy and efficiency of this approach was carried out in a machine learning study. Building upon the success of this technique, implementation of a learning-on-the-fly (active learning) methodology into a workflow to determine accurate material properties with minimal prior knowledge showed great promise, while maintaining a computational demand up to two orders of magnitude lower than comparable first-principles approaches. Investigations of properties of industrially lesser desired, but sometimes present hexagonal alloy phases of Ti1−xAlxN are also included in this thesis, since knowledge and understanding of all competing phases can help guide development toward improving cutting tool lifetime and performance. Furthermore, while w-Ti1−xAlxN may not be able to compete with its cubic counterpart in terms of hardness, it shows promise for other applications due to its electronic and elastic properties. 

Metastable ReNx phases are high energy materials due to their covalent N-N and Re-N bonds, leading to exceptional mechanical and electronic properties. Just like diamond, the hardest and arguably most famous metastable mate-rial naturally occurring on earth, they are stabilized by extreme pressures and high temperatures, but can be quenched to ambient conditions. Understanding the formation and existence of these non-equilibrium compounds may hold the key to unlocking a new generation of hard materials. In this thesis, all currently known phases of ReNx compounds have been investigated, encompassing both experimentally observed and theoretically suggested structures. Investigations of the convex hulls across a broad pressure range were carried out, coupled with calculations of phonons in the proposed crystals to determine both energetic and dynamical stability. Overall, the studies included in this thesis focused mainly on investigation of the ground state of ReN2 at higher pressure, where experimental results were deviating from earlier theoretical predictions. Additional research focused on specifically exploring properties and stability of novel ReN6 at synthesis conditions using the active learning workflow to train an interatomic potential. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. p. 87
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2375
National Category
Condensed Matter Physics
Identifiers
urn:nbn:se:liu:diva-201992 (URN)10.3384/9789180755320 (DOI)9789180755313 (ISBN)9789180755320 (ISBN)
Public defence
2024-04-19, Planck, F-building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2024-04-02 Created: 2024-04-02 Last updated: 2024-04-02Bibliographically approved

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