Automating alloy design and discovery with physics-aware multimodal multiagent AI

成果类型:
Article
署名作者:
Ghafarollahi, Alireza; Buehler, Markus J.
署名单位:
Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
刊物名称:
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN/ISSBN:
0027-9929
DOI:
10.1073/pnas.2414074122
发表日期:
2025-01-28
关键词:
high entropy alloys screw dislocations plasticity dilute films
摘要:
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data- driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLMs) and the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multiobjective design tasks and opens avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability.