An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations.

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries.

Organic nonaqueous redox flow batteries (O-NRFBs) are promising energy storage devices due to their scalability and reliance on sourceable materials. However, finding suitable redox-active organic molecules (redoxmers) for these batteries remains a challenge. Using plant-based compounds as precursors for these redoxmers can decrease their costs and environmental toxicity. In this computational study, flavonoid molecules have been examined as potential redoxmers for O-NRFBs. Flavone and isoflavone derivatives were selected as catholyte (positive charge carrier) and anolyte (negative charge carrier) molecules, respectively. To drive their redox potentials to the opposite extremes, in silico derivatization was performed using a novel algorithm to generate a library of > 40000 candidate molecules that penalizes overly complex structures. A multiobjective Bayesian optimization based active learning algorithm was then used to identify best redoxmer candidates in these search spaces. Our study provides methodologies for molecular design and optimization of natural scaffolds and highlights the need of incorporating expert chemistry awareness of the natural products and the basic rules of synthetic chemistry in machine learning.

Computational Screening of Supported Metal Oxide Nanoclusters for Methane Activation: Insights into Homolytic versus Heterolytic C-H Bond Dissociation.

Since its discovery in zeolites, the [CuOCu]2+ motif has played an important role in our understanding of selective methane activation over supported metal oxide nanoclusters. Although there are two known C-H bond dissociation mechanisms, namely, homolytic and heterolytic cleavage, most computational studies on optimizing metal oxide nanoclusters for improved methane activation reactivity have focused only on the homolytic mechanism. In this work, both mechanisms were examined for a set of 21 mixed metal oxide complexes of the form of [M1OM2]2+ (M1 and M2 = Mn, Fe, Co, Ni, Cu, and Zn). Except for pure copper, heterolytic cleavage was found to be the dominant C-H bond activation pathway for all systems. Furthermore, mixed systems including [CuOMn]2+, [CuONi]2+, and [CuOZn]2+ are predicted to possess methane activation activity similar to pure [CuOCu]2+. These results suggest that both homolytic and heterolytic mechanisms should be considered in computing methane activation energies on supported metal oxide nanoclusters.

Finite Size Effects in Submonolayer Catalysts Investigated by CO Electrosorption on PtsML/Pd(100).

A combination of scanning tunneling microscopy, subtractively normalized interfacial Fourier transform infrared spectroscopy (SNIFTIRS), and density functional theory (DFT) is used to quantify the local strain in 2D Pt clusters on the 100 facet of Pd and its effect on CO chemisorption. Good agreement between SNIFTIRS experiments and DFT simulations provide strong evidence that, in the absence of coherent strain between Pt and Pd, finite size effects introduce local compressive strain, which alters the chemisorption properties of the surface. Though this effect has been widely neglected in prior studies, our results suggest that accurate control over cluster sizes in submonolayer catalyst systems can be an effective approach to fine-tune their catalytic properties.

The critical role of water at the gold-titania interface in catalytic CO oxidation.

We provide direct evidence of a water-mediated reaction mechanism for room-temperature CO oxidation over Au/TiO2 catalysts. A hydrogen/deuterium kinetic isotope effect of nearly 2 implicates O-H(D) bond breaking in the rate-determining step. Kinetics and in situ infrared spectroscopy experiments showed that the coverage of weakly adsorbed water on TiO2 largely determines catalyst activity by changing the number of active sites. Density functional theory calculations indicated that proton transfer at the metal-support interface facilitates O2 binding and activation; the resulting Au-OOH species readily reacts with adsorbed Au-CO, yielding Au-COOH. Au-COOH decomposition involves proton transfer to water and was suggested to be rate determining. These results provide a unified explanation to disparate literature results, clearly defining the mechanistic roles of water, support OH groups, and the metal-support interface.