Graph Machine Learning for Design of High-Octane Fuels

• URL: http://arxiv.org/abs/2206.00619v1
• Date: Wed, 1 Jun 2022 16:43:04 GMT
• Title: Graph Machine Learning for Design of High-Octane Fuels
• Authors: Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, K. Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen
• Abstract summary: Computer-aided molecular design (CAMD) can identify molecules with desired autoignition properties. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization. We experimentally investigate and use to illustrate the need for further auto-ignition training data.
• Score: 47.43758223690195
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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