Machine Learning Prediction of Hydration Free Energy with Physically Inspired Descriptors.

We present machine learning models for predicting experimental hydration free energies of molecules without any atom-, bond-, or geometry-specific input feature. Four types of physically inspired descriptors are adopted for predictions. The first type is composed of the total dipole moment, anisotropic polarizability, and vibrational analysis results of the solute molecule. The second and third types are derived from the electrostatic potential distribution of the solute. The last type includes the solvent accessible surface area and shape similarities. Several machine learning regression models are built on the basis of the FreeSolv database with ∼600 samples, showing a better performance in comparison with that of most traditional approaches and other prediction methods based on molecular fingerprints. In particular, the present descriptors are capable of predicting hydration free energies of new compounds with elements or fragments that are never seen in the training set. The importance of these descriptors, the impact of dissociation energies of specific covalent bonds, and the outliers with relatively large prediction errors are also discussed.

Heterometal-organic frameworks as highly sensitive and highly selective luminescent probes to detect I⁻ ions in aqueous solutions.

Two cationic heterometal-organic frameworks (Eu-Zn (1·NO3⁻) and Tb-Zn (2·NO3⁻)) with NO3(-) counter-anions in the channels are structurally and luminously characterized. Both of them can serve as highly sensitive and highly selective luminescent probes for detecting I(-) ions in aqueous solutions. In particular, 2·NO3⁻ can selectively and reversibly detect I(-) with a fast response time of just 10 s and an extremely low detection limit of 0.001 ppm. Mechanism studies reveal that I(-) is quickly oxidized to form I3⁻with the help of 1·NO3⁻ or 2·NO3⁻, leading to luminescence quenching. This represents the first report of MOF-based luminescent probes for the detection of I(-) in aqueous solutions.

Anion-induced changes of structure interpenetration and magnetic properties in 3D Dy-Cu metal-organic frameworks.

Two novel Dy-Cu MOFs were structurally and magnetically characterized. The results reveal that it is the first example of anion-induced change of structure interpenetration and magnetic properties in 3d-4f MOFs.