HTESP (High-throughput electronic structure package): a Package for the high-throughput $ab$ $initio$ calculations
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910544482533376 |
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| author | Nepal, Niraj K. Canfield, Paul C. Wang, Lin-Lin |
| author_facet | Nepal, Niraj K. Canfield, Paul C. Wang, Lin-Lin |
| contents | High-throughput $ab$ $initio$ calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron-phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different $ab$ $initio$ calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, we introduce a software package named "HTESP" (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HTESP (High-throughput electronic structure package): a Package for the high-throughput $ab$ $initio$ calculations Nepal, Niraj K. Canfield, Paul C. Wang, Lin-Lin Computational Physics High-throughput $ab$ $initio$ calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron-phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different $ab$ $initio$ calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, we introduce a software package named "HTESP" (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes. |
| title | HTESP (High-throughput electronic structure package): a Package for the high-throughput $ab$ $initio$ calculations |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2406.04537 |