Machine Learning Directed Search for Ultraincompressible, Superhard Materials
Machine Learning Directed Search for Ultraincompressible, Superhard Materials
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DOI：
10.1021/jacs.8b02717
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发表时间：
2018-08-08
影响因子：
15
通讯作者：
Brgoch, Jakoah
中科院分区：
文献类型：
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作者：
Tehrani, Aria Mansouri;Oliynyk, Anton O.;Brgoch, Jakoah
在追求具有特殊机械性能的材料时，开发了一种机器学习模型，通过预测弹性模量作为代理来指导合成具有高硬度的化合物。该方法从晶体结构数据库中筛选出由支持向量机回归确定的体积模量和剪切模量最高的118 287种化合物。根据这些模型，选择了一种三元碳化钨铼和一种四元碳化钼钨硼，并在常压下进行了合成。高压金刚石砧细胞的测量结果证实了机器学习对体积模量的预测，误差小于10%，并证实了这两种化合物的超压缩性。随后的维氏显微硬度测量表明，每种化合物在低负荷（0.49 N）下也具有极高的硬度，超过40 GPa的超硬阈值。这些结果表明，通过识别功能无机材料，通过最先进的机器学习技术开发材料是有效的。
In the pursuit of materials with exceptional mechanical properties, a machine-learning model is developed to direct the synthetic efforts toward compounds with high hardness by predicting the elastic moduli as a proxy. This approach screens 118 287 compounds compiled in crystal structure databases for the materials with the highest bulk and shear moduli determined by support vector machine regression. Following these models, a ternary rhenium tungsten carbide and a quaternary molybdenum tungsten borocarbide are selected and synthesized at ambient pressure. High-pressure diamond anvil cell measurements corroborate the machine-learning prediction of the bulk modulus with less than 10% error, as well as confirm the ultraincompressible nature of both compounds. Subsequent Vickers microhardness measurements reveal that each compound also has an extremely high hardness exceeding the superhard threshold of 40 GPa at low loads (0.49 N). These results show the effectiveness of materials development through state-of-the-art machine-learning techniques by identifying functional inorganic materials.