Graph Machine Learning for Design of High-Octane Fuels

Abstract

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.

arXiv commentmanuscript (26 pages, 9 figures, 2 tables), supporting information (12 pages, 8 figures, 1 table)

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Cited by in corpus (4)

• Generative AI and Process Systems Engineering: The Next Frontier
• Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
• Graph Machine Learning for Design of High-Octane Fuels
• GraphXForm: Graph transformer for computer-aided molecular design