人工智能驱动化学品创新设计的实践与展望

It has long been a grand goal for researchers and industry professionals in the chemical engineering community to revolutionize the paradigm of chemical product development and shorten the time from product discovery to application. However, chemical product design is a complex process involving multiple components, scales, and physical fields. It is difficult for existing experimental research models to reveal the relevant physical and chemical mechanisms in depth and efficiently. Therefore, it is necessary to use multi-scale computer simulation technology to predict the properties of chemical products by coupling multi-scale simulation methods starting from the chemical structure at the micro-molecular level. Along with the increasing computing power, “artificial intelligence (AI) -driven” approaches are becoming a significant promise in the pursuit of this objective, where AI is being organically integrated with established multi-scale simulation techniques for efficient and high-fidelity modeling framework with potential for transformative impact on chemical design. For instance, machine learning models trained on high-fidelity multi-scale simulation data can accelerate the prediction of chemical structure-property relationship by orders of magnitude. However, the chemical industry, particularly, the development of new chemical products, presents many unique challenges. The crude application of AI to existing problems and data to construct some predictive models can hardly break the existing bottlenecks fundamentally. Hence, it is imperative to consider how we can integrate AI techniques more effectively and comprehensively with innovative chemical product design. We envision this can be achieved through, e.g., using AI to optimize existing physics-based simulation techniques and efficiently explore hundreds of millions of design parameters to find the best design solutions. Here we discuss the recent development of AI-driven chemical innovation design from three aspects: multi-scale simulation, material design framework, and scientific software development, with an emphasis on the important role of AI technology in achieving the innovation pathway of chemical products. At last, we present our perspective on the current efforts to embrace AI techniques in the engineering of novel chemical product, with the goal of providing a strong foundation to support the advancement of domestic chemical industry.