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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Graph Neural Network Optimization of Industrial Materials, Formulations, and Chemical Processes
Author(s) Ekaterina Gracheva, Kim Karl Limpiada, Vianca Tayag Alegre, Justine Clarisse Valdez, Atsuyuki Nakao, Hisaki Ikebata
On-Site Speaker (Planned) Ekaterina Gracheva
Abstract Scope Industrial materials and chemical processes are governed by interactions among ingredients, processing conditions, and environmental factors. We present an in-house graph neural network framework for modeling and optimizing these systems beyond composition-only prediction. The model encodes materials, process steps, operating parameters, and outcomes in a graph representation, and is pre-trained on a continuously expanding, human-curated dataset of materials and chemistry records prepared by domain specialists. Finetuning on user data combines broad domain knowledge with application-specific behavior. We demonstrate the approach using two industrial cases. In polymer-composite kitchen-sink molding with silica quartz powder, peroxide initiator, and unsaturated polyester resin, the model stabilizes peak time under ambient-temperature variation by adjusting peroxide content. In waterborne silicone emulsion coatings, the model is used for component replacement, predicting formulations that improve water contact angle while reducing surface resistance and dust adhesion within the formulation trade-off. These results illustrate general-purpose GNN optimization for industrial materials processes.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Surface Modification and Coatings, Process Technology

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