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Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.

Cocrystals of a nonionizable, water soluble compound (gababentin lactam (GBPL)) with less soluble coformers, are shown to be 2 to 17 times less soluble than GBPL. Cocrystals of GBPL with gentisic acid, 4-hydroxybenzoic acid, 4-aminobenzoic acid and fumaric acid are characterized by carboxylic acid-amide hydrogen bonds between coformer and GBPL, consistent with a previously reported structure of a benzoic acid cocrystal. The lattice and solvation contributions to cocrystal aqueous solubility were evaluated and solvation was found to be the main contribution to solubilization. Cocrystals exhibited pH-dependent solubility and pH max , both of which are described by coformer pK a and cocrystal K sp values. These findings have important implications for the characterization and selection of cocrystals for desired drug delivery behavior.

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This work challenges the popular notion that pharmaceutical salts are more soluble than cocrystals. There are cocrystals that are more soluble than salt forms of a drug and vice-versa. It all depends on the interplay between the chemistry of both the solid and solution phases. Aqueous solubility, pH, and supersaturation index (SA = S/S or S/S) of cocrystals and salts of a basic drug, lamotrigine (LTG), were determined, and mathematical models that predict the influence of cocrystal/salt K and K were derived. K and SA followed the order LTG-nicotinamide cocrystal (18) > LTG-HCl salt (12) > LTG-saccharin salt (5) > LTG-methylparaben cocrystal (1) > LTG-phenobarbital cocrystal (0.2). The values in parenthesis represent SA under nonionizing conditions. Cocrystal/salt solubility and thermodynamic stability are determined by pH and will drastically change with a single unit change in pH. pH values ranged from 5.0 (saccharin salt) to 6.4 (methylparaben cocrystal) to 9.0 (phenobarbital cocrystal). Cocrystal/salt pH dependence on pK and pK shows that cocrystals and salts exhibit different behavior. Solubility and pH are as important as supersaturation index in assessing the stability and risks associated with conversions of supersaturating forms.