Manura Liyanage^1*, Vladyslav Turlo^1*, W. A. Curtin^2,3*

1 Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, Thun, Switzerland

2 National Centre for Computational Design and Discovery of Novel Materials MARVEL, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne, Switzerland

3 School of Engineering, Brown University, Providence, RI 02906 USA

* Corresponding authors emails: pandula.liyanage@epfl.ch, vladyslav.turlo@empa.ch, william.curtin@epfl.ch

DOI10.24435/materialscloud:1m-0s [version v1]

Publication date: Jul 18, 2024

How to cite this record

Manura Liyanage, Vladyslav Turlo, W. A. Curtin, Machine learning potential for the Cu-W system, Materials Cloud Archive 2024.107 (2024), https://doi.org/10.24435/materialscloud:1m-0s

Description

Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers (NMLs) are finding applications as brazing fillers and shielding material for plasma and radiation. Due to the large lattice mismatch between fcc Cu and bcc W, these systems have complex interfaces that are beyond the scales suitable for ab initio methods, thus motivating the development of chemically accurate interatomic potentials. Here, a neural network potential (NNP) for Cu-W is developed within the Behler-Parrinello framework using a curated training dataset that captures metallurgically-relevant local atomic environments. The Cu-W NNP accurately predicts (i) the metallurgical properties (elasticity, stacking faults, dislocations, thermodynamic behavior) in elemental Cu and W, (ii) energies and structures of Cu-W intermetallics and solid solutions, and (iii) a range of fcc Cu/bcc W interfaces, and exhibits physically-reasonable behavior for solid W/liquid Cu systems. As will be demonstrated in forthcoming work, this near-ab initio-accurate NNP can be applied to understand complex phenomena involving interface-driven processes and properties in Cu-W composites.

Materials Cloud sections using this data

Files

File name	Size	Description
CuW_reference_dataset_Quantun_ESPRESSO.zip MD5md5:948fd4a817d75606c25f5930d27f978a	4.6 GiB	Reference structures used in developing the NNP
NNP_potentials_01-20.zip MD5md5:bfaf300c9ebf9bbef9e2431b79bf79cb	1.0 MiB	All neural network potentials developed in this study
README.txt MD5md5:a595b8d521ae35b4bff6123fe4253a7b	2.6 KiB	Descriptions of the data

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Nanomultilayer Neural network potentials Copper-tungsten MARVEL molecular dynamics simulation