评估固态反应的热力学选择性,用于无机材料的预测合成
Matthew J McDermott1,2, Brennan C McBride3, Corlyn E Regier3
1Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
ACS central science
|October 30, 2023
概括
新的选择性指标通过预测相位形成来指导无机材料合成. 这种数据驱动的方法优化了反应,并发现了像酸 (BaTiO3) 等材料的有效途径.
科学领域:
- 材料科学 材料科学 材料科学
- 固态化学 固态化学
- 计算材料设计设计 计算材料设计
背景情况:
- 无机材料的固态合成缺乏优化反应条件的理论指导.
- 预测目标相形成与杂质形成是材料发现的一个重大挑战.
研究的目的:
- 开发和验证理论指标,以预测固态反应中目标/杂质相形成的有利性.
- 在数据驱动的工作流中实现这些指标,以规划和优化无机材料合成.
- 发现酸泰坦酸 (BaTiO3) 的新和高效的合成途径.
主要方法:
- 引入两个选择性指标:初级竞争和二级竞争.
- 使用这些指标,分析了3520篇关于固态反应的文献.
- 开发一个数据驱动的合成规划工作流程,利用第一原则热力学数据.
- 实验验证使用同步粉X射线衍射的实验验证.
主要成果:
- 选择性指标与固态反应中观察到的目标和杂质形成有效相关.
- 一个数据驱动的工作流确定了82,985种可能的BaTiO3合成反应,导致实验测试了9.
- 通过使用非传统的前体 (BaS/BaCl2和Na2TiO3) 发现了BaTiO3的两种新,高效的合成途径,产生更快的反应时间和更少的杂质.
结论:
- 开发的选择性指标为预测无机合成提供了基础.
- 数据驱动的框架有助于优化现有的合成配方和发现新材料.
- 考虑使用非传统前体的复杂化学物质对于推进材料合成和发现至关重要.
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