6 papers
Data-efficient continuous conditional denoising diffusion model for microstructure generation
Tarakram Ramgopal, Gowtham Nimmal Haribabu, Hussein Farahani +2
Traditional computational models, such as cellular automata and phase-field methods, are effective for simulating microstructural evolution but often face computational bottlenecks…
Deep learning reveals key predictors of thermal conductivity in covalent organic frameworks
Prakash Thakolkaran, Yiwen Zheng, Yaqi Guo +2
The thermal conductivity of covalent organic frameworks (COFs), an emerging class of nanoporous polymeric materials, is crucial for many applications, yet the link between their st…
Piezoelectric truss metamaterials: data-driven design and additive manufacturing
Saurav Sharma, Satya K. Ammu, Prakash Thakolkaran +3
In the development of active animate materials, electromechanical coupling is highly attractive to realize mechanoresponsive functionality. Piezoelectricity is the most utilized el…
Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)
Prakash Thakolkaran, Yaqi Guo, Shivam Saini +3
Traditional constitutive models rely on hand-crafted parametric forms with limited expressivity and generalizability, while neural network-based models can capture complex material…
Toward Sustainable Polymer Design: A Molecular Dynamics-Informed Machine Learning Approach for Vitrimers
Yiwen Zheng, Agni K. Biswal, Yaqi Guo +5
Vitrimer is an emerging class of sustainable polymers with self-healing capabilities enabled by dynamic covalent adaptive networks. However, their limited molecular diversity const…
Experiment-informed finite-strain inverse design of spinodal metamaterials
Prakash Thakolkaran, Michael A. Espinal, Somayajulu Dhulipala +2
Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when desi…