来自全电子混合功能 DFT 计算的材料数据库
Akhil S Nair1,2, Lucas Foppa3, Matthias Scheffler3
1The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195, Berlin, Germany. nair@fhi-berlin.mpg.de.
这项研究使用先进的混合功能计算引入了一个新的无机材料数据库. 这种资源提高了材料发现和人工智能 (AI) 模型预测材料属性的可靠性.
科学领域:
- 计算材料科学
- 材料信息学
- 材料科学中的人工智能
背景情况:
- 材料数据库对于发现具有增强性质的材料至关重要.
- 目前的数据库经常使用通用梯度近似 (GGA),限制某些材料和属性的准确性.
- 这种限制影响了在这些数据库上训练的人工智能 (AI) 模型的可靠性.
研究的目的:
- 使用更准确的计算方法创建无机材料的综合数据库.
- 评估氧化物的热力学和电化学稳定性,用于催化和能源应用.
- 展示这个数据库对训练人工智能模型的有用性.
主要方法:
- 创建一个包含7024种结构和组成不同的无机材料的数据库.
- 在全电子代码 FHI 中实现的混合功能计算旨在创建数据库.
- 在AI模型培训中采用确定独立性选和分散操作员 (SISSO) 方法.
主要成果:
- 通过混合功能计算成功生成了7024种无机材料的新数据库.
- 该数据库可以对氧化物的热力学和电化学稳定性进行可靠的评估.
- 在训练人工智能模型进行材料属性预测时成功应用数据库.
结论:
- 新的数据库采用混合功能计算,提高了材料发现的可靠性.
- 这种资源对于评估催化和能源应用中的氧化物稳定性是有价值的.
- 该数据库有助于开发更准确的材料科学人工智能模型.
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