Abstract submission closed October 31. Authors will be notified of their status in early January.
Symposium CT01—Artificial Intelligence for Material Design, Processing and Characterizations
The life cycle of materials processing involves several steps summarized as design, synthesis, testing/characterization, and application or usage. Traditionally, each step of this life cycle is conducted by humans, including analysis/interpretation of data that is generated during the simulation, experiment, and application as well as decision making. These data often span different length scales of materials from molecular and macroscopic scales and various time scales. They are usually high-dimensional, large in number, and heterogeneous in nature. All of their features make them difficult for researchers to obtain comprehensive insight into these data. Thus, this strictly human-centric material discovery and manufacturing process is clearly neither efficient nor guarantees optimal results. Recently, emerging data-driven techniques based on statistics, machine learning, and artificial intelligence (AI) have shown great potential for improving this process. They can unlock predictions of structures and/or performance of new materials with computations, assist in analyzing and interpreting (potentially extremely large) datasets generated in experiments, inform robots to perform autonomous research from synthesis/processing to structure-property characterizations, and yield operating instructions for manufacturing systems. To widen their application and speed up material and manufacturing innovation, this symposium will explore a broad spectrum of topics in which machine learning, AI, and robotics, which are of increasing importance in addressing challenges in material design, characterization and processing throughout the whole life cycle of materials. The interaction of humans and intelligent systems is a coordinated human-machine partnership. It will bring together researchers from interdisciplinary knowledge domains (computational/experimental materials, engineering, computer science, statistics, and robotics) to discuss the fundamental challenges in applying AI for materials and manufacturing in a close-loop manner as well as its future applications and impact. The broad implications resulting from the fruitful discussions will inspire researchers working across research fields to move forward and promote the basic knowledge development and technology deployment.