固体中的氧化状态来自数据驱动的范式
1Department of Chemistry, Tsinghua University Beijing 100084 China haixiao@tsinghua.edu.cn.
Chemical science
|October 1, 2025
概括
我们介绍了一种数据驱动的方法来计算固体中的氧化状态 (OS),从而能够准确地预测晶体结构. 这种方法为理解化学直觉的计算提供了一种新的方法.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 氧化状态 (OS) 是一个基本的化学概念.
- 从物理定律准确计算操作系统是一项挑战.
- 现有的方法通常依赖于化学直觉而不是计算.
研究的目的:
- 开发一个数据驱动的范式,用于计算晶体结构中的氧化状态.
- 将这个范式作为清华固体中氧化状态 (TOSS) 计算工具来实现.
- 探索用于操作系统预测的机器学习模型.
主要方法:
- 使用贝叶斯最大后期 (MAP) 概率.
- 在大型晶体结构数据集上开发了具有循环结构的TOSS.
- 使用TOSS衍生OS数据训练了一个图形卷积网络 (GCN) 模型.
主要成果:
- 在超过一百万个晶体结构上,TOSS取得了卓越的成功率.
- GCN模型提供了一个替代的,准确的OS预测方法.
- 与精心策划的ICSD数据集进行比较,显示了高准确度 (TOSS为96.09%,GCN为97.24%).
结论:
- 数据驱动的TOSS范式成功计算了氧化状态作为新出现的属性.
- 无论是TOSS还是GCN模型,都为OS确定提供了准确可靠的方法.
- 这项工作展示了数据驱动方法对复杂化学问题的潜力.
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