Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910623959351296 |
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| author | Sheriff, Killian Cao, Yifan Freitas, Rodrigo |
| author_facet | Sheriff, Killian Cao, Yifan Freitas, Rodrigo |
| contents | Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale properties. Establishing chemistry-microstructure relationships in such materials requires proper characterization of these chemical fluctuations. Yet, current characterization approaches (e.g., Warren-Cowley parameters) make only partial use of the complete chemical and structural information contained in local chemical motifs. Here we introduce a framework based on E(3)-equivariant graph neural networks that is capable of completely identifying chemical motifs in arbitrary crystalline structures with any number of chemical elements. This approach naturally leads to a proper information-theoretic measure for quantifying chemical short-range order (SRO) in chemically complex materials, and a reduced - but complete - representation of the chemical space. Our framework enables the correlation of any per-atom property with their corresponding local chemical motif, thereby offering novel avenues to explore structure-property relationships in chemically-complex materials. Using the MoTaNbTi high-entropy alloy as a test system, we demonstrate the versatility of this approach by evaluating the lattice strain associated with each chemical motif, and computing the temperature dependence of chemical-fluctuations length scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks Sheriff, Killian Cao, Yifan Freitas, Rodrigo Materials Science Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale properties. Establishing chemistry-microstructure relationships in such materials requires proper characterization of these chemical fluctuations. Yet, current characterization approaches (e.g., Warren-Cowley parameters) make only partial use of the complete chemical and structural information contained in local chemical motifs. Here we introduce a framework based on E(3)-equivariant graph neural networks that is capable of completely identifying chemical motifs in arbitrary crystalline structures with any number of chemical elements. This approach naturally leads to a proper information-theoretic measure for quantifying chemical short-range order (SRO) in chemically complex materials, and a reduced - but complete - representation of the chemical space. Our framework enables the correlation of any per-atom property with their corresponding local chemical motif, thereby offering novel avenues to explore structure-property relationships in chemically-complex materials. Using the MoTaNbTi high-entropy alloy as a test system, we demonstrate the versatility of this approach by evaluating the lattice strain associated with each chemical motif, and computing the temperature dependence of chemical-fluctuations length scale. |
| title | Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks |
| topic | Materials Science |
| url | https://arxiv.org/abs/2405.08628 |