机器学习对超难压缩材料的定向搜索
Aria Mansouri Tehrani1, Anton O Oliynyk1, Marcus Parry2
1Department of Chemistry , University of Houston , Houston , Texas 77204 , United States.
Journal of the American Chemical Society
|July 17, 2018
概括
机器学习加速了新型超硬材料的发现. 研究人员合成了碳化物和玻璃碳化物,其硬度超过40GPa.
科学领域:
- 材料科学
- 计算材料科学
- 固态化学
背景情况:
- 开发具有特殊机械性能,特别是高硬度的材料是一个重大挑战.
- 预测模型可以加速新型功能性无机材料的发现.
研究的目的:
- 开发一个机器学习模型来预测弹性模块作为材料硬度的代理.
- 识别和合成新的超难压缩和超硬无机化合物.
主要方法:
- 使用支持向量机回归模型从晶体结构数据库选出118,287种化合物.
- 在环境压力下合成了三级碳化物和四级碳化物.
- 进行了高压钻石细胞测量和维克斯微硬度测试.
主要成果:
- 机器学习准确地预测合成化合物的体积模量, 错误率低于10%.
- 这两种合成化合物表现出超不压缩的行为和极高的硬度 (> 40 GPa).
- 鉴定到的材料在低压力下超过了超硬值.
结论:
- 机器学习是加速发现高级功能无机材料的有效策略.
- 开发的模型成功地发现了具有特殊机械性能的新型超硬化合物.
- 这项工作展示了针对材料设计和合成的强大方法.
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