MADAS -- A Python framework for assessing similarity in materials-science data
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929552834428928 |
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| author | Kuban, Martin Rigamonti, Santiago Draxl, Claudia |
| author_facet | Kuban, Martin Rigamonti, Santiago Draxl, Claudia |
| contents | Computational materials science produces large quantities of data, both in terms of high-throughput calculations and individual studies. Extracting knowledge from this large and heterogeneous pool of data is challenging due to the wide variety of computational methods and approximations, resulting in significant veracity in the sheer amount of available data. Here, we present MADAS, a Python framework for computing similarity relations between material properties. It can be used to automate the download of data from various sources, compute descriptors and similarities between materials, analyze the relationship between materials through their properties, and can incorporate a variety of existing machine learning methods. We explain the design of the package and demonstrate its power with representative examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MADAS -- A Python framework for assessing similarity in materials-science data Kuban, Martin Rigamonti, Santiago Draxl, Claudia Materials Science Computational Physics Computational materials science produces large quantities of data, both in terms of high-throughput calculations and individual studies. Extracting knowledge from this large and heterogeneous pool of data is challenging due to the wide variety of computational methods and approximations, resulting in significant veracity in the sheer amount of available data. Here, we present MADAS, a Python framework for computing similarity relations between material properties. It can be used to automate the download of data from various sources, compute descriptors and similarities between materials, analyze the relationship between materials through their properties, and can incorporate a variety of existing machine learning methods. We explain the design of the package and demonstrate its power with representative examples. |
| title | MADAS -- A Python framework for assessing similarity in materials-science data |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2403.10470 |