机器学习支持的Cs/Sr结晶分离的干选
Bingbing Wang1, Zhiyuan Zhang1, Yue Dong1
1School of Chemical Engineering, Sichuan University, Chengdu 610065, China.
Inorganic chemistry
|August 9, 2023
概括
研究人员开发了一个计算框架,以有效地识别用于核燃料再加工的配体. 这种方法加速了用于关键分离过程的新材料的发现,增强了核能可持续性.
科学领域:
- 核化学 核化学 核化学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 可持续的核能依赖于高效的废核燃料再加工.
- 当前的分离技术面临着挑战,需要先进的材料.
- 液体废物中金属的选择性协调至关重要.
研究的目的:
- 开发一个高通量选框架,用于识别核废物分离的有效配体.
- 提高发现下一代分离材料的效率.
- 为了应对废核燃料再加工方面的挑战.
主要方法:
- 使用了结合水溶性,pKa和协调键长度的计算框架.
- 用机器学习模型与图形卷积和转移学习用于属性预测.
- 实施了一个"计算漏斗"来识别适合/分离的配体.
主要成果:
- 成功确定了/结晶分离的潜在配体.
- 机器学习模型准确地预测了连接体的关键化学特征.
- 选择了非有毒和低成本的顶级配体,用于实验验证.
结论:
- 拟议的框架大大提高了用于核废物再处理的配体发现的效率.
- 计算选加速了对关键分离过程有前途的材料的识别.
- 这种方法通过改进燃料循环技术来支持可持续发展的核能.
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