使用机器学习潜力在离子导体中解氧化离子和质子运输
Ying Zhou1, Sacha Fop2, Abbie C Mclaughlin2
1Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK.
概括
机器学习开发了固体电解质的矩张量潜力,可以准确预测离子运输,这对于开发高效,低温固体氧化物燃料电池至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 电化学 电化学 电化学
背景情况:
- 为高效的离子运输开发固体电解质是固体氧化物燃料电池在600°C以下运行的关键.
- 原子模型和机器学习加速了对离子导体的设计和理解.
研究的目的:
- 通过机器学习,为Ba7Nb4MoO20和Sr3V2O8开发精确的矩张力电位 (MTP).
- 根据初始计算和氧化物离子和质子运输的实验数据验证MTP.
主要方法:
- 利用被动和主动学习技术来创建MTP.
- 进行了ab initio分子动力学和密度函数理论计算.
- 将MTP预测与扩散系数和导电性的实验数据进行比较.
主要成果:
- MTPs准确地复制了ab initio分子动力学数据和DFT结果的力量,能量和应力.
- 预测氧化物离子和质子的扩散系数和导电性,与实验非常一致.
- 准确估计了迁移障碍,证明了MTP的稳定性和可转移性.
结论:
- 机器学习衍生的MTP为模拟离子运输提供了计算效率高,准确的方法.
- 这些MTP是设计下一代固体氧化物燃料电池的宝贵工具.
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