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Graph Machine Learning for Design of High-Octane Fuels
- Source :
- arXiv (2022). doi:10.48550/ARXIV.2206.00619, AIChE journal 69(4), e17971 (2023). doi:10.1002/aic.17971, AIChE Journal, 69(4)
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
-
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.<br />Comment: manuscript (26 pages, 9 figures, 2 tables), supporting information (12 pages, 8 figures, 1 table)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Environmental Engineering
Computer and information sciences [FOS]
General Chemical Engineering
graph neural networks
fuel design
computer-aided molecular design
graph machine learning
spark-ignition engines
Machine Learning (cs.LG)
machine learning
ddc:660
renewable fuels
Physics::Chemical Physics
optimization
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 00011541
- Database :
- OpenAIRE
- Journal :
- arXiv (2022). doi:10.48550/ARXIV.2206.00619, AIChE journal 69(4), e17971 (2023). doi:10.1002/aic.17971, AIChE Journal, 69(4)
- Accession number :
- edsair.doi.dedup.....7de5ede5543a4423d71f6da6d1f81158