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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