"胺基 - 氨基 + 酒精 = 碳酸. " 化学反应作为图形神经网络中的线性代数类比
Amer Marwan El-Samman1, Stijn De Baerdemacker1,2
1University of New Brunswick, Department of Chemistry. 30 Dineen Dr Fredericton Canada aelsamma@unb.ca.
Chemical science
|May 21, 2025
概括
化学中的图形神经网络反映了自然语言处理,揭示了原子嵌入中的算术属性,这些属性代表化学反应. 这释放了可解释的深度学习,用于化学洞察和预测.
科学领域:
- 计算化学计算化学
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 化学中的深度学习模型经常充当"黑子",缺乏清晰的解释性.
- 越来越需要了解这些模型的内部机制.
研究的目的:
- 为了证明化学中的图形神经网络 (GNN) 模型表现出类似于自然语言处理 (NLP) 模型的行为.
- 揭示和解释化学数据表示中隐藏的结构.
主要方法:
- 从化学数据的图形模型中获得的分析原子嵌入.
- 将原子嵌入的算术属性与NLP中的文字嵌入进行比较.
- 使用代数操纵来解释观察到的化学类比.
主要成果:
- 原子嵌入显示算术属性,反映化学反应公式,类似于NLP中的单词类比.
- 特定的嵌入载体始终代表化学转换 (例如,酒精到碳).
- 化学类比可以通过局部原子组成和代数操作来解释.
结论:
- 在化学中,GNN具有结构化,可解释的潜在模型,类似于化学语法.
- 这些发现提升了化学中可解释的AI,并使转移学习用于属性预测 (例如NMR) 的应用成为可能.
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