机器学习搜索具有Na,Ca,Cu,Pd和Ag的稳定二进制Sn合金
Aidan Thorn1, Daviti Gochitashvili1, Saba Kharabadze1
1Department of Physics, Applied Physics and Astronomy, Binghamton University, State University of New York, PO Box 6000, Binghamton, New York 13902-6000, USA. kolmogorov@binghamton.edu.
Physical chemistry chemical physics : PCCP
|August 15, 2023
概括
研究人员选了超过200万种M-Sn材料,发现了29种新的稳定金属间物质. 这种机器学习方法加速了对新材料的搜索,这些新材料在储能和电子领域有潜在的应用.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 固态物理 固态物理
背景情况:
- 金属-Sn (M-Sn) 二进制系统以其与储能,电子和超导相关的多种特性而闻名.
- 探索M-Sn系统的庞大的配置空间是计算密集的.
- 以前的研究已经暗示了各种M-Sn化合物的潜力.
研究的目的:
- 在五个M-Sn二进制系统 (M = Na,Ca,Cu,Pd,Ag) 中进行大规模的新合成材料选.
- 在不同温度和压力条件下识别热力学稳定的金属间化合物.
- 为了证明机器学习潜力的有效性,加速材料发现.
主要方法:
- 开发和应用MAISE-NET框架来构建神经网络的原子间潜力.
- 使用MAISE-NET加速*ab initio*全球结构搜索.
- 通过计算选超过200万个候选阶段.
主要成果:
- 在研究的M-Sn系统中发现了29种热力学稳定的金属间物质.
- 预测新的环境压力材料,包括hP6-NaSn2和tI36-PdSn2.
- 在Na-Sn,Cu-Sn和Ag-Sn系统中识别高温,富含锡的基态.
- 重新检查已知的Cu-Sn相,解释它们通过稳定.
结论:
- 该研究成功地确定了许多新的稳定的M-Sn间金属,显著扩大了已知的相位空间.
- 机器学习潜力与*ab initio*方法相结合,为材料发现提供了强大而高效的策略.
- 这些发现为进一步的实验合成和这些新型材料的应用开发提供了基础.
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