图形神经网络用于预测过渡金属复合物的金属-连接物协调
Jacob W Toney1, Roland G St Michel1,2, Aaron G Garrison1
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
我们开发了精确的图形神经网络模型,以预测过渡金属复合体 (TMC) 中的金属-连接体协调. 这通过提高计算选精度来加速新TMC的发现.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 协调化学 协调化学
背景情况:
- 高通量虚拟选 (HTVS) 对于探索过渡金属复合体 (TMC) 是至关重要的.
- 准确的金属连接对有意义的HTVS结果至关重要.
- 现有的方法往往缺乏对协调模式的准确预测.
研究的目的:
- 开发精确的预测模型,用于TMCs的金属合金协调.
- 提高新型TMC计算查的效率和可靠性.
- 将机器学习与现有的选软件集成到实际应用中.
主要方法:
- 策划了来自剑桥结构数据库的70,069个独特连接体的数据集.
- 训练有素的分离图形神经网络 (GNN) 模型来预测协调原子的数量和身份.
- 解释了GNN模型,以获得对协调化学的化学见解.
- 集成的GNN模型与molsimplify软件用于TMC生成.
- 使用密度函数理论 (DFT) 计算验证生成的TMC几何.
主要成果:
- 在预测连接体协调原子的总数和身份方面取得了高准确度和精度.
- 确定了与已建立的协调化学原则相一致的趋势.
- 通过使用集成模型成功生成了1175个过渡金属复合体.
- DFT计算证实了生成的TMC几何形状的有效性.
结论:
- 开发的GNN模型显著改善了金属-合体连接的预测.
- 这些模型通过使金属和配体的新组合能够加速TMC的计算选.
- 这种方法有助于生成物理现实的协调复合体,以便进一步研究.
相关概念视频
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