Using Machine Learning to Parse Chemical Mixture Descriptions

Integrated data-driven cross-disciplinary framework to prevent chemical water pollution

Access to a clean and healthy environment is a human right and a prerequisite for maintaining a sustainable ecosystem. Experts across domains along the chemical life cycle have traditionally operated in isolation, leading to limited connectivity between upstream chemical innovation to downstream development of water-treatment technologies. This fragmented and historically reactive approach to managing emerging contaminants has resulted in significant externalized societal costs. Herein, we propose an integrated data-driven framework to foster proactive action across domains to effectively address chemical water pollution. By implementing this integrated framework, it will not only enhance the capabilities of experts in their respective fields but also create opportunities for novel approaches that yield co-benefits across multiple domains. To successfully operationalize the integrated framework, several concerted efforts are warranted, including adopting open and FAIR (findable, accessible, interoperable, and reusable) data practices, developing common knowledge bases/platforms, and staying vigilant against new substance "properties" of concern.

Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials

Artificial intelligence technology shows the advantages of improving efficiency, reducing costs, shortening time, reducing the number of staff on site and achieving precise operations, making impressive research progress in the fields of drug discovery and development, but there are few reports on application in energetic materials. This paper addresses the high safety risks in the current nitrification process of energetic materials, comprehensively analyses and summarizes the main safety risks and their control elements in the nitrification process, proposes possibilities and suggestions for using artificial intelligence technology to enhance the "essential safety" of the nitrification process in energetic materials, reviews the research progress of artificial intelligence in the field of drug synthesis, looks forward to the application prospects of artificial intelligence technology in the nitrification of energetic materials and provides support and guidance for the safe processing of nitrification in the propellants and explosives industry.