Machine learning aids in the discovery of efficient corrosion inhibitor molecules - Haiyan GONG; Lingwei MA; Dawei ZHANG (工程科学学报)
In recent years, machine learning (ML) has demonstrated significant potential in corrosion inhibitor molecule research and has emerged as a powerful tool for scientists to explore new and efficient corrosion inhibitors. Corrosion inhibitors are chemical substances used to prevent the corrosion of metallic materials, and their effectiveness is crucial for extending equipment lifespan and reducing maintenance costs. However, traditional methods for screening corrosion inhibitor molecules, such as weight loss measurements and electrochemical testing, typically require extensive experiments and considerable time, making them costly. Consequently, the application of ML technology in this field has garnered widespread attention. This review provides an overview of the application of ML technology in screening corrosion inhibitor molecules. Artificial intelligence technologies, particularly deep learning and machine learning, can analyze vast amounts of data on known corrosion inhibitor molecules, to learn and predict the corrosion inhibition performance of new molecules. These technologies not only enhance screening efficiency but also uncover molecular structures and properties that traditional methods may overlook. Specifically, ML models can extract key information and construct predictive models through feature extraction and pattern recognition using existing data. These models can rapidly identify potential high-efficiency corrosion inhibitor molecules, thereby significantly accelerating research. However, despite the numerous advantages of ML technology in screening corrosion inhibitor molecules, its limitations cannot be ignored. First, the current compound search space for corrosion inhibitor molecule screening models remains limited. Second, these models face challenges related to computational resources and time costs in practical applications. After discussing the applications and limitations of ML technology, this study further explores the concept of molecular generation technology and its application in generating corrosion inhibitor molecules. Molecular generation technology employs deep learning techniques for automatically generating new molecular structures, often based on generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). These technologies can learn the rules of molecular generation from existing corrosion inhibitor molecule data and generate new molecules with specific properties. Molecular generation technology can help researchers discover new and efficient corrosion-inhibitor molecules and accelerate the development of new materials. Finally, this paper highlights the challenges faced by generative machine learning models in the discovery of efficient corrosion inhibitor molecules. Although generative models have shown great potential for molecule generation and screening, their application in the discovery of corrosion inhibitors still faces many challenges. For example, generative models require large amounts of high-quality data for training, and the generated results require experimental validation. Moreover, when generating new molecules, generative models must consider various factors, such as molecular stability, synthesizability, and environmental impact, making the design and optimization of these models more complex. Overall, ML technology holds broad application prospects in the research on corrosion inhibitor molecules; however, it also faces significant challenges. Continuously optimizing ML algorithms and combining them with experimental validation should contribute to the efficient and high-precision discovery of corrosion inhibitor molecules in the future, leading to breakthroughs in materials science and industrial applications.