Graph Machine Learning for Design of High-Octane Fuels
This addresses the practical need for efficient fuel design to reduce CO2 emissions, but it is incremental as it builds on existing graph-ML tools.
The paper tackled the problem of designing high-octane fuels for efficient engines by proposing a graph machine learning framework for computer-aided molecular design, which successfully identified known high-octane components and suggested new candidates, with one experimentally investigated.
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.