对DScribe库的更新:新的描述符和衍生词
Jarno Laakso1, Lauri Himanen1, Henrietta Homm1
1Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Finland.
The Journal of chemical physics
|June 20, 2023
概括
现在,DScribe Python包提供了新的材料指纹和描述器衍生品,用于先进的机器学习,增强原子模拟和材料发现.
科学领域:
- 计算材料科学 计算材料科学
- 机器学习用于化学.
- 开发 Python 软件的开发人员.
背景情况:
- 原子描述符对于材料科学中的机器学习模型至关重要.
- 现有的描述器包可能缺乏用于复杂任务的高级功能.
- DScribe 软件包促进了 Python 中原子描述符的使用.
研究的目的:
- 更新 DScribe Python 软件包,添加新的功能.
- 为了实现先进的机器学习任务,如力预测和结构优化.
- 提高原子描述符的准确性和适用性.
主要方法:
- 瓦莱-奥加诺夫材料指纹的整合.
- 实现数值和分析描述函数的导数.
- 开发多体张量表示 (MBTR) 和原子位置平滑重叠 (SOAP) 的导数.
主要成果:
- 现在DScribe包括了Valle-Oganov的指纹.
- 对于所有描述符,数字导数都可用.
- 对于MBTR和SOAP描述器来说,已经实现了分析衍生.
- 衍生品在Cu集群和矿合金上的证明有效性.
结论:
- 更新后的DScribe包增强了材料科学中的机器学习能力.
- 描述器衍生方便先进的预测建模和优化.
- 这些新功能支持更复杂的原子模拟和材料设计.
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