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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2408.06156 |
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| _version_ | 1866914909961322496 |
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| author | Wang, An Sosso, Gabriele C. |
| author_facet | Wang, An Sosso, Gabriele C. |
| contents | Computational scientists have long been developing a diverse portfolio of methodologies to characterise condensed matter systems. Most of the descriptors resulting from these efforts are ultimately based on the spatial configurations of particles, atoms, or molecules within these systems. Noteworthy examples include symmetry functions and the smooth overlap of atomic positions (SOAP) descriptors, which have significantly advanced the performance of predictive machine learning models for both condensed matter and small molecules. However, while graph-based descriptors are frequently employed in machine learning models to predict the functional properties of small molecules, their application in the context of condensed matter has been limited. In this paper, we put forward a number of graph-based descriptors (such as node centrality and clustering coefficients) traditionally utilised in network science, as alternative representations for condensed matter systems. We apply this graph-based formalism to investigate the dynamical properties and phase transitions of the prototypical Lennard-Jones system. We find that our graph-based formalism outperforms symmetry function descriptors in predicting the dynamical properties and phase transitions of this system. These results demonstrate the broad applicability of graph-based features in representing condensed matter systems, paving the way for exciting advancements in the field of condensed matter through the integration of network science concepts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06156 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph-based Descriptors for Condensed Matter Wang, An Sosso, Gabriele C. Computational Physics Disordered Systems and Neural Networks Computational scientists have long been developing a diverse portfolio of methodologies to characterise condensed matter systems. Most of the descriptors resulting from these efforts are ultimately based on the spatial configurations of particles, atoms, or molecules within these systems. Noteworthy examples include symmetry functions and the smooth overlap of atomic positions (SOAP) descriptors, which have significantly advanced the performance of predictive machine learning models for both condensed matter and small molecules. However, while graph-based descriptors are frequently employed in machine learning models to predict the functional properties of small molecules, their application in the context of condensed matter has been limited. In this paper, we put forward a number of graph-based descriptors (such as node centrality and clustering coefficients) traditionally utilised in network science, as alternative representations for condensed matter systems. We apply this graph-based formalism to investigate the dynamical properties and phase transitions of the prototypical Lennard-Jones system. We find that our graph-based formalism outperforms symmetry function descriptors in predicting the dynamical properties and phase transitions of this system. These results demonstrate the broad applicability of graph-based features in representing condensed matter systems, paving the way for exciting advancements in the field of condensed matter through the integration of network science concepts. |
| title | Graph-based Descriptors for Condensed Matter |
| topic | Computational Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2408.06156 |