半导体和介电材料的理论和数据驱动方法:从预测到实验
Fumiyasu Oba1, Takayuki Nagai2, Ryoji Katsube3
1Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.
Science and technology of advanced materials
|December 17, 2024
概括
计算方法加速了新型无机材料的发现,特别是半导体和介电材料. 这些方法结合了理论计算和机器学习,以实现高效的材料设计和性能预测.
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:
- 计算方法在现代材料科学中至关重要.
- 算法的进步,数据的可用性和计算能力增强了这些方法.
- 本综述侧重于用于设计无机材料的计算方法,特别是半导体和介电材料.
研究的目的:
- 审查用于无机材料设计的计算方法的开发和应用.
- 突出使用高通量计算和机器学习来预测材料特性.
- 展示计算机辅助研究在发现功能性材料方面的有效性.
主要方法:
- 高通量第一原理计算用于预测电子结构和属性.
- 机器学习技术用于属性预测,相图构造和材料发现.
- 计算结果与实验合成和表征的整合.
主要成果:
- 准确预测极子,缺陷,表面,接口和散体属性.
- 有效地预测材料属性,并识别具有所需特性的材料.
- 成功的例子包括光电子的三元化物,光伏的化物和铁电矿.
结论:
- 计算方法显著提升了材料设计和预测.
- 这些方法阐明了材料的功能,并指导了实验的努力.
- 计算机辅助研究对于发现和理解新型无机材料是有效的.
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