A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing

Winter, Benedikt; Winter, Clemens; Schilling, Johannes; Bardow, André

doi:10.1039/d2dd00058j

Cited by 39 publications

(56 citation statements)

References 28 publications

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“…In the presented fuel design method, the computationally expensive DFT calculations required by COSMO-RS limit the number of molecule evaluations and thus the exploration of the molecular design space. To investigate more molecules, a future method could employ a hierarchical approach balancing exploration and accuracy, or employ computationally efficient machine learning models also for thermodynamics. − …”

Section: Discussionmentioning

confidence: 99%

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

et al. 2023

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Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel's molecular structure to the needs of SI engines with very high compression ratios, computer-aided molecular design (CAMD) of renewable fuels has received considerable attention over the past decade. To date, CAMD for fuels is typically performed by computationally screening the physicochemical properties of single molecules against property targets. However, achievable SI engine efficiency is the result of the combined effect of various fuel properties, and molecules should not be discarded because of individual unfavorable properties that can be compensated for. Therefore, we present an optimization-based fuel design method directly targeting SI engine efficiency as the objective function. Specifically, we employ an empirical model to assess the achievable relative engine efficiency increase compared to conventional RON95 gasoline for each candidate fuel as a function of fuel properties. For this purpose, we integrate the automated prediction of various fuel properties into the fuel design method: Thermodynamic properties are calculated by COSMO-RS; combustion properties, indicators for environment, health and safety, and synthesizability are predicted using machine learning models. The method is applied to design pure-component fuels and binary ethanol-containing fuel blends. The optimal pure-component fuel tert-butyl formate is predicted to yield a relative efficiency increase of approximately 8% and the optimal fuel blend with ethanol and 3,4-dimethyl-3-propan-2-yl-1-pentene of 19%.

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Section: Discussionmentioning

confidence: 99%

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

et al. 2023

Self Cite

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“…One advantage of our approach is that it can be put into practice, e.g., be integrated into existing process simulators, in a very simple and straightforward manner: one only has to replace the existing UNIFAC parameter set of the model implementation by the predicted one provided with our approach. For other machinelearning approaches, like artificial neural networks operating on molecular graphs 28,29 or SMILES representations of the components, 30 this might be more complicated in practice.…”

Section: Introductionmentioning

confidence: 99%

Prediction of parameters of group contribution models of mixtures by matrix completion

Jirasek

Hayer

Abbas

et al. 2023

Phys. Chem. Chem. Phys.

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“…13 Recently, there has been growing interest in applying ML models to study more complex chemical systems that might contain multiple components such as chemical reactions, 14,15 alloys, 16,17 copolymers, [18][19][20] and gas/liquid mixtures. [21][22][23][24][25][26][27][28][29][30][31][32] Among the ML techniques explored, graph neural networks (GNNs) 33,34 have gained special popularity because they can directly incorporate molecular representations (in the form of graphs), which enable the capturing of key structural information while potentially avoiding the need to pre-calculate/pre-define descriptors using more advanced but computationally-intensive tools such density functional theory (DFT) or molecular dynamics (MD) models.…”

Section: Introductionmentioning

confidence: 99%

Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium

Qin

Jiang

et al. 2022

Preprint

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Graph neural networks (GNNs) have been widely used for predicting molecular properties, especially for single molecules. However, when treating multi-component systems, GNNs have mostly used simple data representations (concatenation, averaging, or self-attention on features of individual components) that might fail to capture molecular interactions and potentially limit prediction accuracy. In this work, we propose a GNN architecture that captures molecular interactions in an explicit manner by combining atomic-level (local) graph convolution and molecular-level (global) message passing through a molecular interaction network. We tested the architecture (which we call SolvGNN) on a comprehensive phase equilibrium case study that aims to predict activity coefficients for a wide range of binary and ternary mixtures; we built this large dataset using the COnductor-like Screening MOdel for Real Solvation (COSMO-RS). We show that SolvGNN can predict composition-dependent activity coefficients with high accuracy and show that it outperforms a previously developed GNN used for predicting only infinite-dilution activity coefficients. We performed counterfactual analysis on the SolvGNN model that allowed us to explore the impact of functional groups and composition on equilibrium behavior. We also used the SolvGNN model for the development of a computational framework that automatically creates phase diagrams for a diverse set of complex mixtures. All scripts needed to reproduce the results are shared as open-source code.

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A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing

Abstract: Knowledge of mixtures' phase equilibria is crucial in nature and technical chemistry. Phase equilibria calculations of mixtures require activity coefficients. However, experimental data on activity coefficients is often limited due...

Cited by 39 publications

References 28 publications

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

Prediction of parameters of group contribution models of mixtures by matrix completion

Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium

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