Mold<sup>2</sup>Molecular Descriptors for QSAR

Hong, Huixiao; Slavov, Svetoslav; Ge, Weigong; Qian, Feng; Su, Zhenqiang; Fang, Hong; Cheng, Yiyu; Perkins, Roger; Shi, Leming; Tong, Weida

doi:10.1002/9783527645121.ch3

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Advanced Comparative Testing Of Degree-based Topological Ind1

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2013

2024

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Cited by 6 publications

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“…216 Similar lists were proposed also elsewhere. [217][218][219] One of these criteria is always that a topological index "should change gradually with gradual change in (molecular) structure". This property may be called the smoothness of the topological index in question.…”

Section: Advanced Comparative Testing Of Degree-based Topological Indmentioning

confidence: 99%

Degree-Based Topological Indices

Gutman¹

2013

Croat. Chem. Acta

645

384

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Section: Advanced Comparative Testing Of Degree-based Topological Indmentioning

confidence: 99%

Degree-Based Topological Indices

Gutman¹

2013

Croat. Chem. Acta

645

384

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Unsupervised Machine Learning‐Derived Anion‐Exchange Membrane Polymers Map: A Guideline for Polymers Exploration and Design

Phua,

Terasoba,

Tanaka

et al. 2024

ChemElectroChem

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Although anion‐exchange membranes (AEMs) are commonly used in fuel cells and water electrolyzers, their widespread commercialization is hindered by problems such as low anion conductivity and durability. Moreover, the development of high‐performance AEMs remains complex and time consuming. Here, we address these challenges by proposing an innovative approach for the efficient design and screening of AEM polymers using unsupervised machine learning. Our model, which combines principal component analysis with uniform manifold approximation and projection, generates an intuitive map that clusters AEM polymers based on structural similarities without any predefined knowledge regarding anion conductivity or other experimentally derived variables. As a powerful navigation tool, this map provides insights into promising main‐chain structures, such as poly(arylene alkylene)s with consistently high conductivity and polyolefins with exceptional performance depending on the substituent. Furthermore, assisted by key molecular descriptors, inverse analysis with this model allows targeted design and property prediction before synthesis, which will significantly accelerate the discovery of novel AEM polymers. This work represents a paradigm shift not only in AEM research but also generally in materials research, moving from black‐box predictions toward interpretable guidelines that foster collaboration between researchers and machine learning for efficient and informed material development.

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