Projects per year
Abstract
We present a general-purpose machine learning Gaussian approximation potential (GAP) for iron that is applicable to all bulk crystal structures found experimentally under diverse thermodynamic conditions, as well as surfaces and nanoparticles (NPs). By studying its phase diagram, we show that our GAP remains stable at extreme conditions, including those found in the Earth's core. The new GAP is particularly accurate for the description of NPs. We use it to identify new low-energy NPs, whose stability is verified by performing density functional theory calculations on the GAP structures. Many of these NPs are lower in energy than those previously available in the literature up to Natoms = 100. We further extend the convex hull of available stable structures to Natoms = 200. For these NPs, we study characteristic surface atomic motifs using data clustering and low-dimensional embedding techniques. With a few exceptions, e.g., at magic numbers Natoms = 59, 65, 76, and 78, we find that iron tends to form irregularly shaped NPs without a dominant surface character or characteristic atomic motif, and no reminiscence of crystalline features. We hypothesize that the observed disorder stems from an intricate balance and competition between the stable bulk motif formation, with bcc structure, and the stable surface motif formation, with fcc structure. We expect these results to improve our understanding of the fundamental properties and structure of low-dimensional forms of iron and to facilitate future work in the field of iron-based catalysis.
Original language | English |
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Article number | 245421 |
Number of pages | 17 |
Journal | Physical Review B |
Volume | 107 |
Issue number | 24 |
DOIs | |
Publication status | Published - 15 Jun 2023 |
MoE publication type | A1 Journal article-refereed |
Fingerprint
Dive into the research topics of 'Searching for iron nanoparticles with a general-purpose Gaussian approximation potential'. Together they form a unique fingerprint.Datasets
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Iron nanoparticle database
Jana, R. (Creator) & Caro Bayo, M. (Creator), Zenodo, 11 Feb 2023
DOI: 10.5281/zenodo.7632314, https://zenodo.org/record/7632315
Dataset
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GAP general purpose interatomic potential for iron
Jana, R. (Creator) & Caro Bayo, M. (Creator), Zenodo, 11 Feb 2023
DOI: 10.5281/zenodo.7630368, https://zenodo.org/record/7630369
Dataset
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NEXTCELL: Next generation interatomic potentials to simulate new cellulose based materials
Caro, M. (Principal investigator)
01/09/2020 → 31/08/2025
Project: Academy of Finland: Other research funding
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COMPEX: Towards accurate computational experimentation: machine-learning-driven simulation of nanocarbon synthesis
Caro, M. (Principal investigator)
01/09/2019 → 31/08/2023
Project: Academy of Finland: Other research funding