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arXiv 2401.09282physics.chem-phcond-mat.mtrl-sci
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从局部原子环境到分子信息熵

From Local Atomic Environments to Molecular Information Entropy

Institute of Physical Chemistry, Friedrich Schiller University Jena(耶拿弗里德里希·席勒大学物理化学研究所)

Alexander Croy

AI总结:

本文提出基于分子相似性矩阵与信息熵的关系,用于衡量分子复杂性,并通过两种方法验证了其有效性。

摘要:

局部原子环境的相似性是许多机器学习技术中的重要概念,广泛应用于计算化学和材料科学。本文提出并讨论了分子相似性矩阵与信息熵之间的联系。所得熵可作为衡量分子复杂性的指标。举例来说,我们介绍了两种定义相似性的具体方法:一种基于局部子结构的SMILES表示,另一种基于SOAP核。通过调节后者的敏感度,可以实现相应熵值之间的良好一致性。最后,我们考虑了混合物中两种分子的熵。由于混合带来的熵增可以作为分子相似性的度量。我们将其与平均值和最佳匹配核进行比较。结果表明不同方法之间存在联系,并展示了基于相似性的熵方法的有用性和广泛应用性。

英文摘要:

The similarity of local atomic environments is an important concept in many machine-learning techniques which find applications in computational chemistry and material science. Here, we present and discuss a connection between the information entropy and the similarity matrix of a molecule. The resulting entropy can be used as a measure of the complexity of a molecule. Exemplarily, we introduce and evaluate two specific choices for defining the similarity: one is based on a SMILES representation of local substructures and the other is based on the SOAP kernel. By tuning the sensitivity of the latter, we can achieve a good agreement between the respective entropies. Finally, we consider the entropy of two molecules in a mixture. The gain of entropy due to the mixing can be used as a similarity measure of the molecules. We compare this measure to the average and the best-match kernel. The results indicate a connection between the different approaches and demonstrate the usefulness and broad applicability of the similarity-based entropy approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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