Authors: Shuai Yang | Jianjun Liu | Fan Jin | Ying Lu
Source: Chinese Science Bulletin (Chinese Version) (article, 2024-07-05)
Field: Materials Science — Materials Chemistry

Integration of artificial intelligence and big data in materials science: New paradigms and scientific discoveries <p indent="0mm">Materials science plays an irreplaceable role in driving societal progress and technological innovation. With the rapid development of artificial intelligence (AI) and big data technologies, the research paradigm in materials science is undergoing profound transformations. This review discusses how the integration of AI and big data is reshaping the research paradigm in materials science (AI for materials science), accelerating the advancement of computational materials science, and innovating the experimental process. It begins by outlining the infrastructure development of material databases in the context of big data, which serve as the cornerstone of scientific work by providing robust support for the storage, management, and analysis of material data. These databases facilitate efficient data handling, enabling researchers to extract valuable insights from vast amounts of experimental and simulation data. Subsequently, the review explores the application of AI technologies across different stages of the material discovery cycle, including theoretical calculations, experimental design, data collection, and synthesis. AI algorithms, particularly deep learning, have revolutionized these stages by enhancing the ability to process and analyze complex datasets, revealing intricate relationships between material structures and their properties. A significant highlight of this review is the introduction of self-driving laboratories (SDLs). Resulting from the integration of AI with laboratory automation and robotics technology, SDLs have realized a complete closed-loop process for material discovery, promoting a significant shift towards autonomous scientific discovery models. These laboratories can independently design and execute experiments, analyze results, and iteratively refine hypotheses, greatly increasing the efficiency and accuracy of material discovery. Furthermore, the development of large language models (LLMs) has brought about revolutionary changes in natural language understanding, leading to the emergence of scientific LLMs, thus expanding the capabilities from text understanding to scientific exploration. The review provides an overview of the latest advancements in LLMs within materials science, emphasizing their critical role in expediting the material discovery process. These models can parse and understand vast amounts of scientific literature, enabling researchers to stay abreast of the latest developments and identify novel research directions. The review concludes by evaluating the challenges involved in building an intelligent ecosystem for material research. These challenges include the need for high-quality, standardized data, the integration of diverse AI tools, and the development of robust methodologies for cross-disciplinary collaboration. Despite these challenges, the substantial potential of AI in materials science is evident. AI technologies promise to transform material research, enabling the discovery of new materials with unprecedented speed and precision. In summary, this review aims to inform researchers about the significance of AI in materials science, highlighting the transformative impact of AI and big data on the research paradigm. It underscores the importance of developing intelligent systems and methodologies to harness the full potential of AI, thereby advancing the field of materials science and contributing to technological innovation and societal progress. Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electric Power Systems and Control