Graph Machine Learning for Design of High-Octane Fuels

TL;DR

This paper introduces a modular graph-ML framework combining generative models, neural networks, and optimization techniques to design molecules with high-octane properties, advancing computer-aided molecular design for fuel development.

Contribution

It presents a novel, integrated graph-ML CAMD framework that effectively designs high-octane molecules, including experimental validation of a new candidate.

Findings

Successfully identified high-octane components

Suggested new candidate molecules for high-octane fuels

Highlighted the need for more auto-ignition training data

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.