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 CO 2 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 autoignition training data.

Identifying key environmental objectives for integrated process and fuel design

“…Most fuel design studies focus on screening molecules or mixtures that lie in a pre-dened range of physico-chemical and combustion-related properties with the aim of identifying a feasible fuel. [1][2][3][4][5][6][7][8] Many of these studies target fuel properties that allow for increased engine efficiency, 1 e.g., through a high octane number, 2,6-8 enthalpy of vaporization, 6,8 laminar burning velocity, 6,8 and compression ratio, 3 which in turn can lead to a reduced global warming impact (GWI). In contrast, only a few studies consider the impact of fuel chemistry on emission formation 1,4,5,8 or the eco-toxicity and human toxicity of the fuel.…”

“…1–8 Many of these studies target fuel properties that allow for increased engine efficiency, 1 e.g. , through a high octane number, 2,6–8 enthalpy of vaporization, 6,8 laminar burning velocity, 6,8 and compression ratio, 3 which in turn can lead to a reduced global warming impact (GWI). In contrast, only a few studies consider the impact of fuel chemistry on emission formation 1,4,5,8 or the eco-toxicity and human toxicity of the fuel.…”

Fuel Ignition Delay Maps for Molecularly Controlled Combustion

“…In fuel design, a fuel is defined by its physicochemical properties, while its molecular structure or composition is considered a degree of freedom. Potential fuel molecules can be compiled from database screenings − or generated using computer-aided molecular design. ,− The properties can be either obtained from databases or predictive models ,,,, or from experiments. , …”

Chemprop: A Machine Learning Package for Chemical Property Prediction

“…Machine learning in general and especially deep learning has become a powerful tool in various fields of chemistry. Applications range from the prediction of physicochemical − and pharmacological properties of molecules to the design of molecules or materials with certain properties, − the exploration of chemical synthesis pathways, − or the prediction of properties important for chemical analysis like IR, UV/vis, or mass spectra. − …”