Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals.

Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms.

[A simulation facility for low temperature water surface environment].

Objective: To simulate the water environment during floating status after astronauts returned and splashed down on the sea because of space capsule emergency in the launching stage.

Method: Environmental temperature in the laboratory and water-temperature in the tank were auto-controlled individually according to heat charge calculation. To meet the need of maintaining the uniformity of water-temperature, the structure of the water circulation pipeline was specially designed to increase thread-conflux and laminar flow effect.