应用大图神经网络来预测过渡金属复合体能量使用tmQM_wB97MV数据集
Aaron G Garrison1, Javier Heras-Domingo1, John R Kitchin1
1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Journal of chemical information and modeling
|December 4, 2023
概括
开发更准确的机器学习 (ML) 模型,用于同质催化,需要更好的数据. 新的tmQM_wB97MV数据集通过解决数据质量问题和纳入各种化学环境,改善了ML模型的预测.
科学领域:
- 计算化学是一种计算化学.
- 材料科学 是一种材料科学.
- 催化剂是一种催化剂.
背景情况:
- 机器学习 (ML) 模型显示了催化剂发现的潜力,但往往缺乏通用性.
- 现有的大型数据集主要用于异质催化,限制了同质催化中的应用.
- tmQM数据集虽然很大,但含有影响ML模型性能的不准确性.
研究的目的:
- 为了创建一个更准确和可泛化的数据集,用于训练ML模型在同质催化.
- 在改进的数据集上评估不同ML模型的性能.
- 调查数据质量和化学多样性对模型准确性的影响.
主要方法:
- 通过纠正结构错误和使用更高层次的DFT (ωB97M-V/def2-SVPD) 重计算能量,开发了tmQM_wB97MV数据集.
- 在tmQM和tmQM_wB97MV数据集上训练和评估各种ML模型 (GemNet-T,PaiNN,SpinConv,SchNet).
- 评估了包括中性与带电物种的影响,并使用预先训练的模型采用了微调策略.
主要成果:
- 与在tmQM_wB97MV上训练的ML模型相比,与在tmQM上训练的ML模型相比,ML模型的预测错误明显较低.
- GemNet-T的表现优于其他模型,其次是PaiNN和SpinConv,然后是SchNet.
- 包括带电物种改善了模型性能,尽管模型仅含中性结构.
- 在异质催化数据 (OC20) 上预先训练的微调模型导致了实质性的性能提升.
结论:
- tmQM_wB97MV数据集显著提高了ML模型对均质催化物的准确性和通用性.
- 数据质量,化学多样性 (包括氧化状态) 和转移学习对于开发强大的ML催化剂至关重要.
- 这些发现表明,朝着更可靠的同质催化剂在中发现的有希望的道路.
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