TY  - JOUR
AU  - Rittig, Jan G.
AU  - Ritzert, Martin
AU  - Schweidtmann, Artur M.
AU  - Winkler, Stefanie
AU  - Weber, Jana M.
AU  - Morsch, Philipp
AU  - Heufer, Karl Alexander
AU  - Grohe, Martin
AU  - Mitsos, Alexander
AU  - Dahmen, Manuel
TI  - Graph machine learning for design of high‐octane fuels
JO  - AIChE journal
VL  - 69
IS  - 4
SN  - 0001-1541
CY  - Hoboken, NJ
PB  - Wiley
M1  - FZJ-2023-00813
SP  - e17971
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000903130000001
DO  - DOI:10.1002/aic.17971
UR  - https://juser.fz-juelich.de/record/917618
ER  -