Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning.

Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric factors are expensive and time intensive; therefore, we developed a machine learning (ML) model that can predict these molecular properties given the SMILES representation of a chemical species. We explored directed message passing neural network (D-MPNN) and graph attention network as ML architecture choices. Additionally, we investigated featurization with additional atomic and molecular features, multitask training, and pretraining using estimated data to optimize model performance. Our final model utilizes a D-MPNN layer to learn the molecular representation and is supplemented by Abraham parameters. A multitask training scheme was used to train a single model to predict all the critical properties and acentric factors along with boiling point, melting point, enthalpy of vaporization, and enthalpy of fusion. The model was evaluated on both random and scaffold splits where it shows state-of-the-art accuracies. The extensive data set of critical properties and acentric factors contains 1144 chemical compounds and is made available in the public domain together with the source code that can be used for further exploration.

Ammonium Formate as a Safe, Energy-Dense Electrochemical Fuel Ionic Liquid.

While solid and liquid energy carriers are advantageous due to their high energy density, many do not meet the efficiency requirements to outperform hydrogen. In this work, we investigate ammonium formate as an energy carrier. It can be produced economically via a simple reaction of ammonia and formic acid, and it is safe to transport and store because it is solid under ambient conditions. We demonstrate an electrochemical cell that decomposes ammonium formate at 105 °C, where it is an ionic liquid. Here, hydrogen evolves at the cathode and formate oxidizes at the anode, both with ca. 100% Faradaic efficiency. Under the operating conditions, ammonia evaporates before it can oxidize; a second, modular device such as an ammonia fuel cell or combustion engine is necessary for complete oxidation. Overall, this system represents an alternative class of electrochemical fuel ionic liquids where the electrolyte is majority fuel, and it results in a modular release of hydrogen with potentially zero net-carbon emissions.