论文标题
机器学习和超智B-C-N化合物的进化预测
Machine Learning and Evolutionary Prediction of Superhard B-C-N Compounds
论文作者
论文摘要
我们建立随机森林模型，以预测化合物的弹性特性和机械硬度，仅使用其化学式作为输入。模型训练使用10,000多种目标化合物和60个功能，基于化学计量属性，元素特性，轨道职业和离子键合水平。使用模型，我们为B-C-N化合物构造三角形图来绘制其散装和剪切模量以及硬度值。这些图表明1：1 b-n比可以导致各种超胸组成。我们还通过进化结构预测和密度功能理论来验证机器学习结果。我们的研究表明，BC $ _ {10} $ n，b $ _4 $ c $ _5 $ n $ _3 $和b $ _2 $ _2 $ c $ _3 $ n具有动态稳定的稳定阶段，具有硬度> 40 $ gpa，这可能是可能由低 - 薄膜plasma plasma plasma Metages组成的潜在的新超级hard材料。
We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. Using the models, we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli, as well as hardness values. The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions. We also validate the machine learning results by evolutionary structure prediction and density functional theory. Our study shows that BC$_{10}$N, B$_4$C$_5$N$_3$, and B$_2$C$_3$N exhibit dynamically stable phases with hardness values $>40$GPa, which are potentially new superhard materials that could be synthesized by low-temperature plasma methods.
