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AI-accelerated materials informatics method for the discovery of ductile alloys

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Abstract

In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants, misfit volumes, etc.), representative for the macroscopic behavior. The material properties are usually computed using special quasi-random structures, in tandem with density functional theory (DFT). However, DFT scales cubically with the number of atoms and is thus impractical for a screening over many alloy compositions. Here, we present a novel methodology which combines modeling approaches and machine-learning interatomic potentials. Machine-learning interatomic potentials are orders of magnitude faster than DFT, while achieving similar accuracy, allowing for a predictive and tractable high-throughput screening over the whole alloy space. The proposed methodology is illustrated by predicting the room temperature ductility of the medium-entropy alloy Mo–Nb–Ta.

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Data availability

The implementation of the MTPs is included in the MLIP package which is publicly available for academic use at https://mlip.skoltech.ru/download/ upon registration. Additional scripts, necessary to run our algorithm, as well as the training data, are available from the authors upon reasonable request.

Notes

  1. In principle, the model also depends on other orientations, but it was shown in [26] that the {112} stacking fault plane and the {110} surface plane are the most important ones for Mo–Nb–Ta.

  2. Otherwise the active learning algorithm may erroneously undersample certain compositions not considered in the initial training set.

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Acknowledgments

This work was supported by the Russian Science Foundation (Grant Number 18-13-00479). MH would further like to thank W. A. Curtin and B. Yin for helpful discussions during the initial stage of this project. Moreover, the authors would like to thank P. Andric for providing his scripts for computing the Stroh tensors for the K-factors. The figures in this article showing atomistic configurations were created using the visualization software OVITO [56].

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Authors and Affiliations

  1. Skolkovo Institute of Science and Technology (Skoltech), Center for Artificial Intelligence Technology, Moscow, Russia

    I. Novikov, A. Shapeev & M. Hodapp

  2. Moscow Institute of Physics and Technology (MIPT), Moscow, Russia

    I. Novikov & O. Kovalyova

  3. Materials Center Leoben Forschung GmbH (MCL), Leoben, Austria

    M. Hodapp

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  1. I. Novikov
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  2. O. Kovalyova
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Novikov, I., Kovalyova, O., Shapeev, A. et al. AI-accelerated materials informatics method for the discovery of ductile alloys. Journal of Materials Research 37, 3491–3504 (2022). https://doi.org/10.1557/s43578-022-00783-z

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