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Found 3 papers, 1 papers with code
Streamlining Energy Transition Scenarios to Key Policy Decisions
Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon.
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
In recent times, machine learning has joined the more established property prediction models.
A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing
This training strategy enables SPT to accurately predict limiting activity coefficients even for unknown molecules, cutting the mean prediction error in half compared to state-of-the-art models for activity coefficient predictions such as COSMO-RS, UNIFAC, and improving on recent machine learning approaches.
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