Predicting elastic properties of hard-coating alloys using ab-initio and machine learning methods

Accelerated design of hard-coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models. We develop a heavily automated high-throughput workflow to build a database of industrially relevant hard-coating materials, such as binary and ternary nitrides. We use the high-throughput toolkit to automate the density functional theory calculation workflow. We present results, including elastic constants that are a key parameter determining mechanical properties of hard-coatings, for X1-xYxN ternary nitrides, where X,Y ∈ {Al, Ti, Zr, Hf} and fraction . We also explore ways for machine learning to support and complement the designed databases. We find that the crystal graph convolutional neural network trained on ordered lattices has sufficient accuracy for the disordered nitrides, suggesting that existing databases provide important data for predicting mechanical properties of qualitatively different types of materials, in our case disordered hard-coating alloys.Funding Agencies|Competence Center Functional Nanoscale Materials (FunMat-II) (Vinnova)Vinnova [2016-05156]; Knut and Alice Wallenberg FoundationKnut &amp; Alice Wallenberg Foundation [KAW-2018.0194]; Swedish Government Strategic Research Areas in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-LiU) [2009 00971]; Russian Science FoundationRussian Science Foundation (RSF) [18-12-00492]; Swedish Research Council (VR)Swedish Research Council [2020-05402]; Swedish e-Science Centre (SeRC); Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-05973]</p

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

Data-driven approaches are becoming increasingly valuable for modern science, and they are making their way into industrial research and development (R&amp;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.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]</p

Totuuden sanoja : valikoima kapinanaikaisia "maanalaisia" kirjoituksia

Kirjoittajain luettelo: Juhani Aho, Santeri Alkio, Einar Böök, Matti Helenius-Seppälä, Ellen Key, Paavo Korpisaari, Hannes Leiviskä, S. Levämäki, Aarno Malin, Lauri Mäkinen, Bertel Nyberg, A. Ruokosalmi, Elli Tavastähti, W. W. Tuomioja