Computational Materials Science & AI
LLComputations for Materials – Discovery, Design and the Role of AI
The integration of materials simulation, autonomous experiments, data science, and machine learning approaches from physics-inspired to large language models is transforming modern materials design and discovery. This symposium brings together global leaders in data driven materials research (modelling and experiments) as well as artificial intelligence experts to present and discuss the latest achievements in the field.
Scope:
The large-scale deployment of first-principles electronic structure calculations, in combination with improvements of machine learning models, large language models, and development of self-driving laboratories, is setting the stage for a new paradigm in modern materials science. The sequential discovery process where materials are in-silico discovered and afterward tested in a laboratory is now being replaced at an accelerated pace by an intertwined process where materials modelling and self-driving robotic experiments are tightly coupled via machine learning and more recently large language models. This coupling enables discovery and testing of materials on the fly, reducing the discovery time and making materials search and selection more efficient. To truly realize the vision of accelerated materials discovery and optimization, new materials modelling techniques, machine learning models, and autonomous experimentation are needed. Further, these elements need to be connected via seamless data infrastructures. The main goal of this symposium is to gather leading scientists and engineers from academia, national labs and industry to discuss the status and the outlook for research and applications of computation and data-driven materials science, with an emphasis on the experimental validation and the integration of theory, computations, artificial intelligence, and experiment. The common challenges and opportunities will be at the focus of the discussions. The symposium will cover a wide range of studies including advancements in theory, computational methods (including high-throughput and AI, machine (deep) learning), the role of data in modern materials science, and materials synthesis and characterization for accelerated design and discovery.
Hot topics to be covered by the symposium:
- Materials Design and Discovery
- Data Infrastructure
- Materials Acceleration Platforms
- Energy Materials
- Multiscale modelling
- Multiscale modelling
- Autonomous synthesis and characterization
- Data mining, Machine Learning and Large Language Models in Materials Research
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