用于将电池电解质的结构组成映射到设备性能的配方图表.
Vidushi Sharma1, Maxwell Giammona1, Dmitry Zubarev1
1IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States.
Journal of chemical information and modeling
|November 10, 2023
概括
一种新的深度学习模型,即配方图卷积网络 (F-GCN),可以准确预测液体配方特性. 这种计算方法通过绘制结构-组成关系,加速了先进材料的发现,如电池电解质.
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
- 机器学习 机器学习
背景情况:
- 发现新的配方,就像先进的材料一样,是具有挑战性的.
- 目前的方法在很大程度上依赖于实验室实验来优化配方.
- 组件的高通量选可以加速化合物发现,但不能加速配方选择.
研究的目的:
- 开发一种深度学习模型,用于预测液体配方特性.
- 建立一个计算方法来映射结构-构成关系到配方性能.
- 加速新型配方的发现和开发.
主要方法:
- 开发了配方图形卷积网络 (F-GCN) 模型.
- 利用并行图形卷积网络 (GCNs) 来特色化配方组成部分.
- 综合成分描述符,按摩尔百分比缩放,成一个组合配方描述符.
- 采用HOMO-LUMO和电势特征的知识转移,用于增强的分子描述器.
主要成果:
- F-GCN模型准确地预测了电池电解质的性能指标.
- 在预测库伦比效率 (CE) 和特定容量方面实现了最低报告的错误.
- 在两个不同的电池电解质配方数据集上证明了有效性.
- 性能最好的模型使用了分子图形描述器,由电子属性告知.
结论:
- F-GCN模型为配方发现提供了一个强大的计算工具.
- 这种深度学习方法显著减少了对配方实验选的依赖.
- 能够更快地开发高性能材料,特别是在储能应用中.
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