层次交叉规模的机器学习用于通过金属氧化物材料提升除去的解释和预测
Yuchen Wei1, Shiyun Xu1, Wenwen Xu1
1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.
Environmental research
|January 24, 2026
概括
一个新的机器学习 (ML) 框架准确地预测金属氧化物去除. 这种跨度模型集成了分子细节,改进了水治理技术设计.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 污染需要先进的水处理解决方案.
- 金属氧化物材料显示出潜力,但由于复杂的多层次因素而面临挑战.
- 传统的机器学习 (ML) 模型可能表现出解释性偏差,忽视了关键的纳米/分子尺度机制.
研究的目的:
- 开发一个跨度层次的ML框架,克服传统ML方法在预测金属氧化物去除方面的局限性.
- 整合一个机制意识的描述器,选择性指数 (PSI),以弥合分子/纳米信息与操作规模的动力学.
- 提高ML模型对去除的预测准确度和机械相关性.
主要方法:
- 开发了一个跨度层次的ML框架,包含选择性指数 (PSI).
- 在数量上,PSI捕捉了结构-反应性关系,并与DFT吸附能相关联 (皮尔森R=0.72).
- 将跨尺度层次模型 (CSO模型) 与基本的多尺度模型 (MSO模型) 的性能进行比较.
主要成果:
- 在预测去除动力学 (log(k) 方面,CSO模型显著优于MSO模型,实现了更高的测试组准确性 (R2=0.77对比0.69).
- 将PSI纠正的偏见解释嵌入到基本的多尺度模型中,并提高了预测准确性.
- 该CSO模型在具有挑战性的酸盐和有机酸盐上显示出较低的预测误差,这表明其适用性更广泛.
结论:
- 开发的跨度层次和机制意识的ML框架对消除的材料的预测设计具有前景.
- 这种方法可以准确预测水处理功能材料的速率.
- 这项研究推动了微观洞察力转化为水资源整治的实用工程应用.
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