This work presents an approach to aid the discovery of inorganic solids by highlighting regions of underexplored yet likely productive composition space using machine learning. A support vector regression algorithm was constructed to determine a compound's formation energy based solely on chemical composition using 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies were then used to construct zero-kelvin convex hull diagrams and identify compositions on the hull and +50 meV above the convex hull. Using this methodology, Y-Ag-Tr (Tr = B, Al, Ga, In) ternary diagrams were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation of the predictions. A particularly promising but unexplored region in the Y-Ag-In diagram was identified, and the ensuing solid-state synthesis produced YAg_0.65In_1.35, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In and confirm the crystal structure solution.

这项工作提出了一种方法，该方法通过使用机器学习突出显示未充分开发但可能具有生产性的合成空间的区域，从而帮助发现无机固体。构建了支持向量回归算法，仅使用313,965个高通量密度泛函理论计算，即可根据化学成分确定化合物的形成能。然后，将得到的预测的编队能量用于构建零开尔文凸壳图，并识别该壳上的成分以及凸壳上方的+50 meV。使用这种方法，Y-Ag- Tr（Tr 由于化学的多样性是triel元素的函数，因此探索了B，Al，Ga，In）三元图，从而提供了对这些预测的实验验证。在Y-Ag-In图中确定了一个特别有希望但尚未探索的区域，随后的固态合成产生了_1.35的YAg _0.65 In ，尚未报道。最后，通过第一性原理计算确定Ag和In的顺序，并确定晶体结构。