| Official site | https://github.com/s-nanjo/Spacier |
|---|---|
| Openness | ★★★ |
| Manual | https://github.com/s-nanjo/Spacier?tab=readme-ov-file#usage |
| Download | https://github.com/s-nanjo/Spacier?tab=readme-ov-file#installation |
| License |
BSD 3-Clause License |
| Core Developers |
南條舜（総合研究大学院大学）, Arifin（JSR株式会社、RDテクノロジー・デジタル変革センター） |
| Availability |
– Python 3.8+ – pip (Python package manager) |
| Related Papers |
Shun Nanjo, Arifin, Hayato Maeda, Yoshihiro Hayashi, Kan Hatakeyama-Sato, Ryoji Himeno, Teruaki Hayakawa, Ryo Yoshida, “SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines” npj Computational Materials volume 11, Article number: 16 (2025)
|
Spacier
Spacier is a Python package providing tools for data exploration and analysis of molecular spatial data, particularly in the field of polymers. In this package, various sampling methods, such as Expected Improvement (EI), Probability of Improvement (PI), and Expected Hypervolume Improvement (EHVI), and optimization techniques, including Bayesian Optimization (BO), are available. After calculation, spacier prepares the submission scripts of computational jobs for RadonPy simulations to various queuing systems.
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