Georgia Institute of Technology
Assembly Line
A federated learning approach to automated and secure supplier selection in cyber manufacturing as-a-service
The emergence of cyber or platform-based manufacturing as-a-service is rapidly disrupting the way discrete parts are sourced and manufactured. However, the centralized business model of cyber manufacturing as-a-service platforms raises concerns about data ownership and access control of independent manufacturing suppliers. Contrary to centralized platforms, cyber manufacturing as-a-service aims to connect designers with geographically distributed manufacturers by serving as a broker who matches the query part design requirements with the manufacturing capabilities of candidate suppliers in its network. One of the key challenges in realizing the vision of cyber manufacturing as-a-service is the lack of a computationally efficient method for manufacturing capability search while maintaining data security of the proprietary datasets of the suppliers in the network. In this paper, we propose a federated learning approach that utilizes a deep unsupervised part retrieval model (FL-DUPR) to learn a federated embedding of suppliers’ manufacturing capabilities without directly accessing their proprietary datasets. We demonstrate through two case studies that this approach yields a supplier selection accuracy of 89 % when the manufacturing capabilities of the suppliers do not overlap, and a multi-label supplier selection accuracy of 87 % when there are significant overlaps in the suppliers’ manufacturing capabilities. We also show that our unsupervised learning approach outperforms the baseline supervised learning classification model trained under the same federated learning framework. The results demonstrate the promise of the proposed federated embedding approach for automated identification of the required manufacturing capabilities offered by various suppliers without directly accessing their proprietary data, thus paving the way for a more secure cyber manufacturing as-a-service business model.
LLMatDesign: Autonomous Materials Discovery with Large Language Models
Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials, but these methods still depend heavily on very large quantities of training data and often lack the flexibility and chemical understanding often desired in materials discovery. We introduce LLMatDesign, a novel language-based framework for interpretable materials design powered by large language models (LLMs). LLMatDesign utilizes LLM agents to translate human instructions, apply modifications to materials, and evaluate outcomes using provided tools. By incorporating self-reflection on its previous decisions, LLMatDesign adapts rapidly to new tasks and conditions in a zero-shot manner. A systematic evaluation of LLMatDesign on several materials design tasks, in silico, validates LLMatDesign’s effectiveness in developing new materials with user-defined target properties in the small data regime. Our framework demonstrates the remarkable potential of autonomous LLM-guided materials discovery in the computational setting and towards self-driving laboratories in the future.