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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 Data-Driven Surrogate to FE for Predicting Elastoplastic Constitutive Relation in Polymer Nanocomposites
Author(s) Dibya Jyoti Basu, Seongmin Yoon, Vikas Tomar
On-Site Speaker (Planned) Dibya Jyoti Basu
Abstract Scope Phenomenological constitutive relations describe the macroscopic response of materials but generally remain unknown for newly developed material systems. Their identification typically requires computationally expensive inverse Finite-Element (FE) calculations that iteratively calibrate constitutive parameters against experimental observations. In this work, we develop a data-driven framework that infers the bulk plasticity law from compositional and microstructural descriptors alone, bypassing per-formulation inverse modeling. Using epoxy nanocomposites as a model system, we train the framework on FE simulations with a cohesive-zone representation of damage and show how nanoscale modification reorganizes the macroscopic flow response: adding nanoclay progressively suppresses the plastic work density and drives a ductile-to-brittle transition from neat epoxy to its modified counterparts. The inferred constitutive laws reproduce the measured stress–strain response across compositions spanning this transition, with no per-material fitting. The result is a predictive microstructure-to-plasticity map for polymer nanocomposites and a route to the inverse design of constitutive behavior from composition.
Proceedings Inclusion? Planned:
Keywords Polymers, Computational Materials Science & Engineering, Other

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