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  1. Free, publicly-accessible full text available October 11, 2024
  2. Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas previous machine learning approaches to ATPS prediction used physicochemical properties of each solute as a descriptor, in this work, we show how to impute missing miscibility outcomes directly from an incomplete collection of pairwise miscibility experiments. We use graph-regularized logistic matrix factorization (GR-LMF) to learn a latent vector of each solution from (i) the observed entries in the pairwise miscibility matrix and (ii) a graph where each node is a solution and edges are relationships indicating the general category of the solute (i.e., polymer, surfactant, salt, protein). For an experimental data set of the pairwise miscibility of 68 solutions from Peacock et al. [ACS Appl. Mater. Interfaces 2021, 13, 11449–11460], we find that GR-LMF more accurately predicts missing (im)miscibility outcomes of pairs of solutions than ordinary logistic matrix factorization and random forest classifiers that use physicochemical features of the solutes. GR-LMF obviates the need for features of the solutions and solutions to impute missing miscibility outcomes, but it cannot predict the miscibility of a new solution without some observations of its miscibility with other solutions in the training data set. 
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    Free, publicly-accessible full text available September 8, 2024
  3. Free, publicly-accessible full text available September 12, 2024
  4. Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs.

     
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    Free, publicly-accessible full text available January 1, 2025
  5. The title compound, [Al 4 (CH 3 ) 8 (C 2 H 7 N) 2 H 2 ], crystallizes as eight-membered rings with –(CH 3 ) 2 Al–(CH 3 ) 2 N–(CH 3 ) 2 Al– moieties connected by single hydride bridges. In the X-ray structure, the ring has a chair conformation, with the hydride H atoms being close to the plane through the four Al atoms. An optimized structure was also calculated by all-electron density functional theory (DFT) methods, which agrees with the X-ray structure but gives a somewhat different geometry for the hydride bridge. Charges on the individual atoms were determined by valence shell occupancy refinements using MoPro and also by DFT calculations analyzed by several different methods. All methods agree in assigning a positive charge to the Al atoms, negative charges to the C, N, and hydride H atoms, and small positive charges to the methyl H atoms. 
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  6. The discoveries that will advance science in profound ways will be made possible by collaborative, multidisciplinary efforts. These efforts require practices and incentives for sharing methods and data, and for leveraging complementary capabilities.

     
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  7. Despite its simplicity, the composition of a material can be used as input to machine learning models to predict a range of materials properties. However, many property optimization tasks require the generation of novel but realistic materials compositions. In this study, we describe a way to generate compositions of hybrid organic–inorganic crystals through adapting Augmented CycleGAN, a novel generative model that can learn many-to-many relations between two domains. Specifically, we investigate the problem of composition change upon amine swap: for a specific chemical system (set of elements) crystalized with amine A, how would the product chemical compositions change if it is crystalized with amine B? By training with limited data from Cambridge Structural Database, our model can generate realistic chemical compositions for hybrid crystalline materials. The Augmented CycleGAN model can also utilize abundant unpaired data (compositions of different chemical systems), a feature that traditional supervised methods lack. The generated compositions can be used for many tasks, for example, as input fed to a classifier that predicts structural dimensionality. 
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