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<p>As a rapid, scalable, and eco-friendly synthesis method, the self-propagating high-temperature synthesis (SHS) is widely applied for ceramics, however, whose development is always limited by high experimental costs. To address this challenge, a workflow for its feasibility is established by combining first-principles calculations and machine learning to quickly predict SHS reactions of MAX and MAB phases. Based on feasibility criteria of SHS (adiabatic combustion temperature <i>T</i><sub>ad</sub> &gt; 1800 K), 60 MAX and 19 MAB phases are predicted to be feasible for direct-ignition SHS under ideal adiabatic assumptions, with 17 experimentally validated ones. Furthermore, some high-<i>T</i><sub>ad</sub> phases are successfully synthesized by SHS, confirming the practical utility of the calculated thermodynamic properties and <i>T</i><sub>ad</sub>. It follows that all the data of <i>T</i><sub>ad</sub> as well as elemental properties are fed to train a Random Forest Regression model and a SISSO-derived analytical model. Moreover, the synergistic effect of low-VEC transition metals and high-VEC main-group elements significantly improves the heat release performance. Of much interest, a new MAB phase V<sub>5</sub>PB<sub>2</sub> is experimentally discovered by SHS with the aid of machine-learning models. This screening workflow is expected to be a valuable tool for future large-scale synthesis and optimization of reaction conditions for materials.</p>

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Machine learning screening the feasibility for self-propagating reactions of the MAX and MAB phases with ab initio dataset

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

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  • Hang Yin,
  • Wanjun Yu,
  • Yongdong Yu,
  • Ali Saffar Shamshirgar,
  • Johanna Rosen,
  • Yun Fan,
  • Zhiyao Lu,
  • Jinze Zhang,
  • Kebin Qin,
  • Guangping Song,
  • Yongting Zheng,
  • Xiaodong He,
  • Hongming Zhang,
  • Yuelei Bai

摘要

As a rapid, scalable, and eco-friendly synthesis method, the self-propagating high-temperature synthesis (SHS) is widely applied for ceramics, however, whose development is always limited by high experimental costs. To address this challenge, a workflow for its feasibility is established by combining first-principles calculations and machine learning to quickly predict SHS reactions of MAX and MAB phases. Based on feasibility criteria of SHS (adiabatic combustion temperature Tad > 1800 K), 60 MAX and 19 MAB phases are predicted to be feasible for direct-ignition SHS under ideal adiabatic assumptions, with 17 experimentally validated ones. Furthermore, some high-Tad phases are successfully synthesized by SHS, confirming the practical utility of the calculated thermodynamic properties and Tad. It follows that all the data of Tad as well as elemental properties are fed to train a Random Forest Regression model and a SISSO-derived analytical model. Moreover, the synergistic effect of low-VEC transition metals and high-VEC main-group elements significantly improves the heat release performance. Of much interest, a new MAB phase V5PB2 is experimentally discovered by SHS with the aid of machine-learning models. This screening workflow is expected to be a valuable tool for future large-scale synthesis and optimization of reaction conditions for materials.