Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning
| Autor: | Turab Lookman, Graeme J. Ackland, Xiangdong Ding, Ghanshyam Pilania, Hongxiang Zong |
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| Rok vydání: | 2018 |
| Předmět: |
Materials science
Complex system chemistry.chemical_element Interatomic potential 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Molecular dynamics Phase (matter) 0103 physical sciences lcsh:TA401-492 General Materials Science 010306 general physics Phase diagram lcsh:Computer software Zirconium business.industry 021001 nanoscience & nanotechnology Computer Science Applications Bond length Transformation (function) lcsh:QA76.75-76.765 chemistry Mechanics of Materials Modeling and Simulation lcsh:Materials of engineering and construction. Mechanics of materials Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | npj Computational Materials Zong, H, Pilania, G, Ding, X, Ackland, G J & Lookman, T 2018, ' Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning ', npj Computational Materials, vol. 4, no. 1, 48 . https://doi.org/10.1038/s41524-018-0103-x npj Computational Materials, Vol 4, Iss 1, Pp 1-8 (2018) |
| ISSN: | 2057-3960 |
| DOI: | 10.1038/s41524-018-0103-x |
| Popis: | Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding. Based on machine-learning (ML) techniques, we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations (AIMD). Using zirconium as a model system, for which an adequate semiempirical potential describing the phase transformation process is lacking, we demonstrate the feasibility and effectiveness of our approach. Specifically, the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons, elastic constants, as well as stacking fault energies. Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium. Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by Hongxian Zong at Xi’an Jiaotong University, China, and Turab Lookman at Los Alamos National Laboratory, U.S.A, used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium. They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume. The resulting machine-learning potential successfully described pure zirconium’s physical properties. When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations. Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems. |
| Databáze: | OpenAIRE |
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