Journal Article

FZJ-2023-00813

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Graph machine learning for design of high‐octane fuels

Rittig, J. G.RWTH* ; Ritzert, M. ; Schweidtmann, A. M. ; Winkler, S.RWTH* ; Weber, J. M. ; Morsch, P.RWTH* ; Heufer, K. A.RWTH* ; Grohe, M.RWTH* ; Mitsos, A.FZJ*RWTH* ; Dahmen, M. (Corresponding author)FZJ*

2023
Wiley Hoboken, NJ

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Please use a persistent id in citations: http://hdl.handle.net/2128/34205  doi:10.1002/aic.17971

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 autoignition training data.

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Contributing Institute(s):

1. Modellierung von Energiesystemen (IEK-10)

Research Program(s):

1. 1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112) (POF4-112)

Appears in the scientific report 2023

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Wiley ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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