التفاصيل البيبلوغرافية
| العنوان: |
EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics |
| المؤلفون: |
Aravind Krishnamoorthy, Ankit Mishra, Deepak Kamal, Sungwook Hong, Ken-ichi Nomura, Subodh Tiwari, Aiichiro Nakano, Rajiv Kalia, Rampi Ramprasad, Priya Vashishta |
| المصدر: |
SoftwareX, Vol 13, Iss , Pp 100663- (2021) |
| بيانات النشر: |
Elsevier, 2021. |
| سنة النشر: |
2021 |
| المجموعة: |
LCC:Computer software |
| مصطلحات موضوعية: |
Molecular dynamics, Interatomic forcefield, Genetic algorithm, Global optimization, Computer software, QA76.75-76.765 |
| الوصف: |
Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2352-7110 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S235271102100008X; https://doaj.org/toc/2352-7110 |
| DOI: |
10.1016/j.softx.2021.100663 |
| URL الوصول: |
https://doaj.org/article/19eafe76c03a4798b6d12bd2fe9a3ac6 |
| رقم الانضمام: |
edsdoj.19eafe76c03a4798b6d12bd2fe9a3ac6 |
| قاعدة البيانات: |
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