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A team of chemists and computer scientists have developed an ecosystem of tools to boost machine-learning-based design of metal-organic frameworks. Reticular chemistry is the science of designing and synthesizing porous crystalline materials with certain predefined structures and properties. This system is designed to reduce the time-consuming and costly trial-and-error experiments associated with the discovery and optimization of new MOFs. The tools convert data into machine learning inputs to create a system to boost machine-learning frameworks.
