Data-driven methods to design, model, and interpret complex materials across scales

Abstract

Deep learning has revolutionized scientific and engineering applications by enabling fast, data-driven predictions and optimizations. In materials science, however, its impact is limited by complex structure–property relationships, sparse high-quality data, and the need to respect fundamental physical laws. Overcoming these challenges calls for machine learning frameworks that learn effectively from limited data while producing results that are physically meaningful, interpretable, and actionable.
This thesis develops physics-guided machine learning approaches to accelerate the design, modeling, and interpretation of materials across scales and material classes. It introduces neural network architectures for metamaterial design that learn directly from data while remaining physically consistent. These models retrieve structural designs that achieve specified target properties instantly, enabling rapid exploration of the structure–property landscape and supporting scenarios with multiple design goals more efficiently than traditional optimization methods. For material modeling, this work addresses the limitation that stress fields are not directly accessible in experiments. The proposed frameworks instead learn constitutive laws from measurable quantities such as displacements and forces, while preserving essential physical principles such as thermodynamic consistency. This work further demonstrates how data-driven approaches reveal previously unknown structure–property relationships, such as the role of dangling atomic masses in the thermal transport of crystalline nanoporous materials. Finally, it introduces a chemistry-constrained generative framework that proposes synthesizable, diverse, and novel molecular structures using limited data, while providing interpretable representations of the molecular generation.
Together, these contributions establish that physics-guided machine learning can complement and extend traditional materials science by delivering reliable, interpretable, and generalizable solutions to longstanding challenges in design and modeling.