Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials.

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transferable; i.e., the MLIP can be applied to mixtures of ions not directly trained on, while only being trained on a few mixtures of the same ions. We also investigated the accuracy of MLIPs for a novel IL, which we experimentally synthesize and characterize. Our MLIP trained on ∼200 DFT frames is in reasonable agreement with our experiments and DFT.

Design Principles for Using Amphiphilic Polymers To Create Microporous Water.

Aqueous dispersions of microporous nanocrystals with dry, gas-accessible pores─referred to as "microporous water"─enable high densities of gas molecules to be transported through water. For many applications of microporous water, generalizable strategies are required to functionalize the external surface of microporous particles to control their dispersibility, stability, and interactions with other solution-phase components─including catalysts, proteins, and cells─while retaining as much of their internal pore volume as possible. Here, we establish design principles for the noncovalent surface functionalization of hydrophobic metal-organic frameworks with amphiphilic polymers that render the particles dispersible in water and enhance their hydrolytic stability. Specifically, we show that block co-polymers with persistence lengths that exceed the micropore aperture size of zeolitic imidazolate frameworks (ZIFs) can dramatically enhance ZIF particle dispersibility and stability while preserving porosity and >80% of the theoretical O2 carrying capacity. Moreover, enhancements in hydrolytic stability are greatest when the polymer can form strong bonds to exposed metal sites on the external particle surface. More broadly, our insights provide guidelines for controlling the interface between polymers and metal-organic framework particles in aqueous environments to augment the properties of microporous water.

Generalizable Synthesis of Highly Fluorinated Ionic Liquids.

The unique chemistry of fluorocarbons (in particular, their weak intermolecular interactions and high degree of intrinsic free volume) makes them promising building blocks for ionic liquids with high gas capacities. Here, we report a generalizable method for the synthesis of fluorinated ionic liquids, which relies on the evolution of gaseous byproducts to drive product formation. This synthetic strategy overcomes solubility challenges that can hinder the synthesis of highly fluorinated ionic liquids via conventional methods and enables a systematic investigation of the effect of fluorination on ionic liquid viscosity and gas solubility.

Designing Glass and Crystalline Phases of Metal-Bis(acetamide) Networks to Promote High Optical Contrast.

Owing to their high tunability and predictable structures, metal-organic materials offer a powerful platform to study glass formation and crystallization processes and to design glasses with unique properties. Here, we report a novel series of glass-forming metal-ethylenebis(acetamide) networks that undergo reversible glass and crystallization transitions below 200 °C. The glass-transition temperatures, crystallization kinetics, and glass stability of these materials are readily tunable, either by synthetic modification or by liquid-phase blending, to form binary glasses. Pair distribution function (PDF) analysis reveals extended structural correlations in both single and binary metal-bis(acetamide) glasses and highlights the important role of metal-metal correlations during structural evolution across glass-crystal transitions. Notably, the glass and crystalline phases of a Co-ethylenebis(acetamide) binary network feature a large reflectivity contrast ratio of 4.8 that results from changes in the local coordination environment around Co centers. These results provide new insights into glass-crystal transitions in metal-organic materials and have exciting implications for optical switching, rewritable data storage, and functional glass ceramics.

Microporous water with high gas solubilities.

Liquids with permanent microporosity can absorb larger quantities of gas molecules than conventional solvents1, providing new opportunities for liquid-phase gas storage, transport and reactivity. Current approaches to designing porous liquids rely on sterically bulky solvent molecules or surface ligands and, thus, are not amenable to many important solvents, including water2-4. Here we report a generalizable thermodynamic strategy to preserve permanent microporosity and impart high gas solubilities to liquid water. Specifically, we show how the external and internal surface chemistry of microporous zeolite and metal-organic framework (MOF) nanocrystals can be tailored to promote the formation of stable dispersions in water while maintaining dry networks of micropores that are accessible to gas molecules. As a result of their permanent microporosity, these aqueous fluids can concentrate gases, including oxygen (O2) and carbon dioxide (CO2), to much higher densities than are found in typical aqueous environments. When these fluids are oxygenated, record-high capacities of O2 can be delivered to hypoxic red blood cells, highlighting one potential application of this new class of microporous liquids for physiological gas transport.

Understanding Relationships between Free Volume and Oxygen Absorption in Ionic Liquids.

Understanding the factors that govern gas absorption in ionic liquids is critical to the development of high-capacity solvents for catalysis, electrochemistry, and gas separations. Here, we report experimental probes of liquid structure that provide insights into how free volume impacts the O2 absorption properties of ionic liquids. Specifically, we establish that isothermal compressibility─measured rapidly and accurately through small-angle X-ray scattering─reports on the size distribution of transient voids within a representative series of ionic liquids and is correlated with O2 absorption capacity. Additionally, O2 absorption capacities are correlated with thermal expansion coefficients, reflecting the beneficial effect of weak intermolecular interactions in ionic liquids on free volume and gas absorption capacity. Molecular dynamics simulations show that the void size distribution─in particular, the probability of forming larger voids within an ionic liquid─has a greater impact on O2 absorption than the total free volume. These results establish relationships between the ionic liquid structure and gas absorption properties that offer design strategies for ionic liquids with high gas solubilities.

Metal-Organic Phase-Change Materials for Thermal Energy Storage.

The development of materials that reversibly store high densities of thermal energy is critical to the more efficient and sustainable utilization of energy. Herein, we investigate metal-organic compounds as a new class of solid-liquid phase-change materials (PCMs) for thermal energy storage. Specifically, we show that isostructural series of divalent metal amide complexes featuring extended hydrogen bond networks can undergo tunable, high-enthalpy melting transitions over a wide temperature range. Moreover, these coordination compounds provide a powerful platform to explore the specific factors that contribute to the energy density and entropy of metal-organic PCMs. Through a systematic analysis of the structural and thermochemical properties of these compounds, we investigated the influence of coordination bonds, hydrogen-bond networks, neutral organic ligands, and outer-sphere anions on their phase-change thermodynamics. In particular, we identify the importance of high densities of coordination bonds and hydrogen bonds to achieving a high PCM energy density, and we show how metal-dependent changes to the local coordination environment during melting impact the entropy and enthalpy of metal-organic PCMs. These results highlight the potential of manipulating order-disorder phase transitions in metal-organic materials for thermal energy storage.

Machine-learning-assisted materials discovery using failed experiments.

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.