论文标题
用于设计高辛烷值燃料的图形机器
Graph Machine Learning for Design of High-Octane Fuels
论文作者
论文摘要
具有高敲击性的燃料使现代的火花点击发动机能够达到高效率，从而使二氧化碳排放率较低。因此，具有高研究辛烷值和高辛烷值灵敏度指示的所需自动点特性的分子的鉴定具有很大的实际相关性，并且可以通过计算机辅助分子设计（CAMD）支持。 Graph Machine Learne（Graph-ML）领域的最新发展为CAMD提供了新颖，有希望的工具。我们提出了一个模块化的Graph-ML CAMD框架，该框架将生成图ML模型与图形神经网络和优化整合在一起，从而在连续分子空间中具有所需的点火特性的分子设计。特别是，我们探讨了贝叶斯优化和遗传算法与生成图-ML模型结合使用的潜力。 Graph-ML CAMD框架成功地识别了建立的高辛烷值成分。它还提出了新的候选人，其中之一是我们实验研究和使用，以说明需要进一步的自动点击培训数据。
Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data.
