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Development of an interatomic potential for the W–Ta system

Journal Article · · Computational Materials Science
 [1];  [2];  [2];  [2];  [3];  [2]
  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States). Physics Division; Texas A & M University, College Station, TX (United States)
  2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States). Physics Division
  3. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States). Materials Science Division
A physics-inspired, data-driven interatomic potential framework for multi-element system is presented. This potential is based on the generalization of the embedded atom method potential and systematically incorporates two-, three- and many-body effects. Different atomic environments are described by atom-centered Gaussian basis sets which provides sufficient flexibility to capture diverse atomic environments and allows for easy optimization of the free parameters. An interatomic potential model for the tungsten–tantalum (W–Ta) system is developed using this framework by training on data from ab initio density functional theory (DFT) calculations. The potential is thoroughly tested at various compositions by comparing bulk and defect properties from experimental and DFT data. It is shown that the potential predicts the elastic constants, defect properties, such as vacancy and interstitial formation energies, core structures of dislocations, and melting points for both pure and tantalum and tungsten with an accuracy comparable to other single element machine learning based potentials. In conclusion, the potential model is used to investigate the formation energies of ordered alloys and vacancy formation energies for chemically random W–Ta alloys.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
2280497
Alternate ID(s):
OSTI ID: 2369727
Report Number(s):
LLNL-JRNL-855988; 1084519
Journal Information:
Computational Materials Science, Journal Name: Computational Materials Science Vol. 230; ISSN 0927-0256
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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