Molecular Machine Learning in Chemical Process Design 1. This perspective article explores the potential of molecular machine learning (ML) in chemical process engineering, highlighting its ability to predict molecular properties and design new molecules with high accuracy. The integration of ML into chemical process design could significantly accelerate the identification of novel molecules and processes. 2. The article reviews state-of-the-art molecular ML models, including graph neural networks (GNNs) and transformers, which have shown remarkable performance in predicting properties of both pure components and mixtures. These models often outperform traditional methods in chemical engineering, such as UNIFAC and COSMO-RS. 3. A key innovation discussed is the incorporation of physicochemical knowledge into ML models in a hybrid or physics-informed manner. This approach not only enhances the accuracy of predictions but also ensures thermodynamic consistency, which is crucial for practical applications. 4. The authors advocate for leveraging molecular ML at the chemical process scale, which remains largely unexplored. They propose integrating ML models into process design and optimization formulations, suggesting that this could lead to more sustainable and efficient chemical processes. 5. The article emphasizes the importance of creating benchmarks and validating proposed molecular candidates in collaboration with the chemical industry. This would help in establishing practical standards and ensuring the reliability of ML-driven molecular design. 6. The authors also highlight the potential of generative ML models for computer-aided molecular design (CAMD), which can explore the chemical space beyond traditional methods. This opens up new possibilities for discovering molecules with desired properties. 7. The article concludes by calling for strong collaboration between academia and industry to improve the development and reliability of molecular ML models. This collaboration is seen as essential for advancing the practical application of these models in industrial settings. 📜Paper: arxiv.org/abs/2508.20527v1 #MolecularMachineLearning #ChemicalProcessDesign #GraphNeuralNetworks #Transformers #AIinChemistry #SustainableChemistry

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