从局部原子环境到分子信息.
1Institute of Physical Chemistry, Friedrich Schiller University Jena, 07737 Jena, Germany.
ACS omega
|May 13, 2024
概括
我们引入了信息的方法来量化分子复杂性和相似性. 这种方法连接了当地的原子环境,使得计算化学和材料科学中的新应用成为可能.
科学领域:
- 计算化学是一种计算化学.
- 材料科学 是一种材料科学.
- 机器学习 机器学习
- 化学信息学 化学信息学
背景情况:
- 当地原子环境的相似性对于化学和材料的机器学习至关重要.
- 量化分子复杂性和相似性对于预测性质和设计新材料至关重要.
研究的目的:
- 建立信息和分子相似性矩阵之间的连接.
- 提出作为分子复杂性的度量和用于比较混合物中的分子的工具.
主要方法:
- 根据当地的原子环境相似性计算分子.
- 使用SMILES表示和SOAP内核进行相似性定义.
- 分析分子混合物中的变化,以评估分子相似性.
主要成果:
- 开发了一种基于的分子复杂性的测量方法.
- 通过调整参数,证明了不同基于相似性的计算之间的良好一致性.
- 展示了混合作为分子相似度的实用性.
结论:
- 信息为分子复杂性和相似性提供了一种多用途的衡量标准.
- 拟议的方法连接了各种相似度指标,并显示了在计算化学和材料科学中的广泛应用.
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