Select ionic liquids (ILs) dissolve significant quantities of cellulose through disruption and solvation of inter- and intramolecular hydrogen bonds. In this study, thermodynamic solid-liquid equilibrium was measured with microcrystalline cellulose in a model IL, 1-ethyl-3-methylimidazolium diethyl phosphate ([EMIm][DEP]) and mixtures with protic antisolvents and aprotic cosolvents between 40 and 120 °C. The solubility of cellulose in pure [EMIm][DEP] exhibits an asymptotic maximum of approximately 20 mass % above 100 °C. Solubility studies conducted on antisolvent mixtures with [EMIm][DEP] and [BMIm][Cl] indicate that protic solvents, ethanol, methanol, and water, significantly reduce the cellulose capacity of IL mixtures by 38-100% even at small antisolvent loadings (<5 mass %). Alternatively, IL-aprotic cosolvent (dimethyl sulfoxide, dimethylformamide, and 1,3-dimethyl-2-imidazolidinone) mixtures at mass ratios up to 1:1 enhance cellulose dissolution by 20-60% compared to pure [EMIm][DEP] at select temperatures. Interactions between the IL and molecular solvents were investigated by Kamlet-Taft solvatochromic analysis, FTIR, and NMR spectroscopy. The results indicate that preferential solvation of the IL cation and anion by co- and antisolvents impact the ability of IL ions to interact with cellulose thus affecting the cellulose dissolution capacity of IL-solvent mixtures.

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium-oxide polymorphs of IrO 2 and IrO 3 . The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB 2 and AB 3 prototypes in existing materials databases (more than 38,000). Next, using an active learning approach we are able to find 196 IrO 2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO 3 polymorphs and report a previously unknown FeF 3 -type structure as the most stable, termed α-IrO 3 . To test the algorithms performance, we compare to a random search of the candidate space and report at least a twofold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO 2 and IrO 3 with less than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low energy structures. Subsequent Pourbaix Ir-H 2 O analysis shows that α-IrO 3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO 2 . Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO 3 than on any other considered iridium-oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal-oxide structure with a defined stoichiometry.

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to...