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Our manuscript, entitled "Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model" has been selected for the front cover of the ACS Journal of Chemical Information and Modeling.
The AI-driven hourglass symbolizes retention time in chromatography, showcasing the application of advanced technology to analyze and predict the time molecules spend in the stationary phase. This is achieved by using simplified molecular input line entry system (SMILES) sequences as textual input to predict retention times. This image was primarily generated with the assistance of Google’s Gemini.
Mazraedoost S, Sedigh Malekroodi H, Žuvela P, Yi M, Liu JJ. Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model. Journal of Chemical Information and Modeling. 2025, 65, 7, 3343–3356.
Our manuscript, entitled "Life Cycle Assessment of Inland Green Hydrogen Supply Chain Networks with Current Challenges and Future Prospects" has been selected for the front cover of the ACS Sustainable Chemistry & Engineering.
A green hydrogen supply chain network is evaluated through an environmental sustainability perspective through a detailed life cycle assessment study of different hydrogen supply routes, highlighting the main bottlenecks and possible future opportunities for the development of a hydrogen mobility infrastructure.
Akhtar MS, Dickson R, Liu JJ. Life cycle assessment of inland green hydrogen supply chain networks with current challenges and future prospects. ACS Sustainable Chemistry & Engineering. 2021 Dec 2;9(50):17152-63.
https://doi.org/10.1021/acssuschemeng.1c06769
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