Scholarlink

<p>We introduce CG-Vec, a crystal graph-to-vector framework that replaces iterative message passing with compact, interpretable descriptors coupled to conventional machine learning. Across diverse datasets, CG-Vec matches the accuracy of deep graph networks on large datasets but substantially outperforms them in data-scarce regimes. The advantage is most pronounced for magnetic properties, where existing approaches have struggled: CG-Vec delivers reliable predictions of magnetization in both ferromagnetic and ferrimagnetic systems and of Curie temperature. Beyond magnetism, CG-Vec performs competitively for formation energy and band gap, demonstrating broad applicability. These results establish vectorized representations as a practical and scalable alternative to deep architectures, enabling efficient and interpretable modeling of complex material properties.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A crystal graph to vector approach for predicting magnetic properties

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月11日

查看全文
  • Sandeep Singh,
  • Abhishek Sharma,
  • Arti Kashyap

摘要

We introduce CG-Vec, a crystal graph-to-vector framework that replaces iterative message passing with compact, interpretable descriptors coupled to conventional machine learning. Across diverse datasets, CG-Vec matches the accuracy of deep graph networks on large datasets but substantially outperforms them in data-scarce regimes. The advantage is most pronounced for magnetic properties, where existing approaches have struggled: CG-Vec delivers reliable predictions of magnetization in both ferromagnetic and ferrimagnetic systems and of Curie temperature. Beyond magnetism, CG-Vec performs competitively for formation energy and band gap, demonstrating broad applicability. These results establish vectorized representations as a practical and scalable alternative to deep architectures, enabling efficient and interpretable modeling of complex material properties.