mlsanityData to 14 Oct 2026

Graph Machine Learning for Design of High-Octane Fuels

arXiv 2206.00619 ↗First posted 1 Jun 2022cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 45 of its 166 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

45 of 166 references.

References · 45

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33 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 3.

6 papers.

  1. 2024Generative AI and Process Systems Engineering: The Next Frontiercited by 9
  2. 2022Physical Pooling Functions in Graph Neural Networks for Molecular Property Predictioncited by 7
  3. 2024GraphXForm: Graph transformer for computer-aided molecular designcited by 6
  4. 2025Molecular Machine Learning in Chemical Process Designcited by 5
  5. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 3
  6. 2025A Generative Deep Learning Workflow for Inverse Molecular Design of Fuelscited by 0