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<p>Advances in materials databases create an opportunity to uncover descriptors that predict emergent properties, yet most studies rely on high-throughput ab initio calculations that can diverge from experiment. Experimentalists instead depend on intuition honed by hands-on work. We present “Materials Expert-Artificial Intelligence” (ME-AI), a machine-learning framework that translates this intuition into quantitative descriptors extracted from curated, measurement-based data. Using a set of 879 square-net compounds described using 12 experimental features, we train a Dirichlet-based Gaussian-process model with a chemistry-aware kernel. ME-AI reproduces established expert rules for spotting topological semimetals (TSMs) and reveals hypervalency as a decisive chemical lever in these systems. Remarkably, a model trained only on square-net TSM data correctly classifies topological insulators in rocksalt structures, demonstrating transferability. Complementing electronic-structure theory, our framework scales with growing databases, embeds expert knowledge, offers interpretable criteria, and guides targeted synthesis, accelerating materials discovery and rapid experimental validation across diverse chemical families.</p>

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Materials Expert-Artificial Intelligence for materials discovery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

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  • Yanjun Liu,
  • Milena Jovanovic,
  • Krishnanand Mallayya,
  • Wesley J. Maddox,
  • Andrew Gordon Wilson,
  • Sebastian Klemenz,
  • Leslie M. Schoop,
  • Eun-Ah Kim

摘要

Advances in materials databases create an opportunity to uncover descriptors that predict emergent properties, yet most studies rely on high-throughput ab initio calculations that can diverge from experiment. Experimentalists instead depend on intuition honed by hands-on work. We present “Materials Expert-Artificial Intelligence” (ME-AI), a machine-learning framework that translates this intuition into quantitative descriptors extracted from curated, measurement-based data. Using a set of 879 square-net compounds described using 12 experimental features, we train a Dirichlet-based Gaussian-process model with a chemistry-aware kernel. ME-AI reproduces established expert rules for spotting topological semimetals (TSMs) and reveals hypervalency as a decisive chemical lever in these systems. Remarkably, a model trained only on square-net TSM data correctly classifies topological insulators in rocksalt structures, demonstrating transferability. Complementing electronic-structure theory, our framework scales with growing databases, embeds expert knowledge, offers interpretable criteria, and guides targeted synthesis, accelerating materials discovery and rapid experimental validation across diverse chemical families.