深度学习框架的原子级设计和预合成预测的硬币金属纳米集群
Jiayi Wang1, Chunwei Dong2, Xiaochuan Gou3
1Center of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
ACS central science
|February 2, 2026
概括
我们开发了CoLiM,一个深度神经网络,在实验之前预测合成硬币金属纳米集群 (CMN) 的化学兼容性. 这种框架使精确的原子级改造成为可能,并加速了新型纳米集群的发现.
科学领域:
- 纳米材料科学 科学 纳米材料科学
- 计算化学计算化学
- 化学中的人工智能.
背景情况:
- 硬币金属纳米集群 (CMN) 提供精确的结构属性关系,但它们的合成依赖于试错.
- 预先设计的CMN的确定性合成 (反向合成) 由于合成后的结构确定性而具有挑战性.
- 目前的方法缺乏在实验之前指导合成的预测能力.
研究的目的:
- 引入CoLiM,一个深度神经网络框架,用于在CMN合成之前预测化学兼容性.
- 为了使纳米集群的逆合成和精确的原子级改造.
- 加速新型CMN的理性发现和设计.
主要方法:
- 开发了一个具有双编码器架构的深度神经网络 (CoLiM).
- 在1989年的数据集上训练了CoLiM,报告了CMN结构和气相集群数据.
- 在持有测试集上使用曲线下的面积 (AUC) 度量验证了CoLiM的预测性能.
主要成果:
- 最优的CoLiM模型实现了AUC超过0.83,超过了基线方法.
- CoLiM成功指导了铜纳米团的单原子编辑,合成了预先设计的结构.
- 在实体实验条件下证明了框架的通用性和实际实用性.
结论:
- CoLiM通过预测合成前的化学兼容性,促进了CMN的逆合成.
- 该框架允许精确的原子级改造纳米集群结构.
- CoLiM显著加速了合理的纳米集群发现和设计.
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