mlsanityData to 14 Oct 2026

Molecular Machine Learning in Chemical Process Design

arXiv 2508.20527 ↗First posted 28 Aug 2025physics.chem-ph cs.LGcited by 5

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Abstract (arXiv)

We present a perspective on molecular machine learning (ML) in the field of chemical process engineering. Recently, molecular ML has demonstrated great potential in (i) providing highly accurate predictions for properties of pure components and their mixtures, and (ii) exploring the chemical space for new molecular structures. We review current state-of-the-art molecular ML models and discuss research directions that promise further advancements. This includes ML methods, such as graph neural networks and transformers, which can be further advanced through the incorporation of physicochemical knowledge in a hybrid or physics-informed fashion. Then, we consider leveraging molecular ML at the chemical process scale, which is highly desirable yet rather unexplored. We discuss how molecular ML can be integrated into process design and optimization formulations, promising to accelerate the identification of novel molecules and processes. To this end, it will be essential to create molecule and process design benchmarks and practically validate proposed candidates, possibly in collaboration with the chemical industry.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 54 of its 130 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

54 of 130 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026Tabular foundation models for in-context prediction of molecular propertiescited by 6
  2. 2026Differentiable Thermodynamic Phase-Equilibria for Machine Learningcited by 1
  3. 2025DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction taskscited by 0
  4. 2026Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibriacited by 0
  5. 2026A Systematic Evaluation of Molecular Mixture Behavior Predictioncited by 0