基于机器学习的高合金发现
Ziyuan Rao1, Po-Yen Tung1,2, Ruiwen Xie3
1Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany.
概括
我们开发了一种积极的学习策略, 这种方法迅速发现了具有非常低热膨胀的两种合金,加速了材料的发现.
科学领域:
- 材料科学
- 金属工程
- 计算材料科学
背景情况:
- 高合金 (HEAs) 具有传统材料无法获得的独特特性.
- 由于传统热力学规则的巨大组成空间和局限性,设计高温电站具有挑战性.
- 发现具有特定性质的高温电池通常依赖于偶然性.
研究的目的:
- 为了加速设计和发现高的英华合金.
- 开发一种积极的学习策略,
- 确定具有非常低的热膨胀系数的高温电池.
主要方法:
- 用密度函数理论,热力学计算和实验验证进行集成的积极学习.
- 采用闭环方法进行代材料设计和表征.
- 在稀疏的数据上使用机器学习选数百万个潜在的组合.
主要成果:
- 发现了两种新型的高英华合金.
- 实现了极低的热膨胀系数 (大约 2 × 10−6 K−1 在300 K).
- 在高维空间中证明了主动学习策略的有效性.
结论:
- 提出的积极学习策略可以快速和自动地发现HEAs.
- 这种方法适用于优化热,磁和电特性.
- 这些合金代表了低热膨胀材料的重大进步.
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