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Smart polymers, also known as stimulus-responsive polymers or environmentally sensitive polymers, are a class of polymers that exhibit changes in their physical and chemical properties in response to various external stimuli, including small physical and chemical changes in the environment. These stimuli can trigger changes in the properties of the smart polymer, such as phase, shape, optics, mechanics, electric field, surface energy, reaction rate, permeability, and perception. Due to the biological sluggishness of conventional smart polymers, different manifestations of polymers are realized through the combination of AI technologies, including water solubility, adsorption on the surface of the carrier, or part of the cross-linked polymer system. While the definition of smart polymers can include two-phase transition processes such as glass transition and melting, the focus of research in the field of smart polymer systems is their behavior in polymer aqueous solutions, interfaces, and hydrogels. In this paper, a noteworthy smart polymer Low critical solution temperature (LCST) polymer is analyzed based on the combination of AI algorithm and deep learning. By carefully designing and modifying the structure of the polymer, the researchers can adjust the LCST to approximate the physiological temperature, making it suitable for potential biomedical applications. Looking ahead, an important development direction is the creation of LCST-type polymers that exhibit a variety of responses to different stimuli, while also improving their biodegradability. Incorporating AI into the design and modification of these polymers could facilitate the development of advanced smart materials with enhanced properties and functionality, opening up new possibilities in areas such as biotechnology, drug delivery, and response materials.
He et al. (Sun,) studied this question.