cff-version: 1.2.0
abstract: "<p>Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance. While their elastic mechanical properties have been systematically determined, their large-deformation, nonlinear responses have been challenging to predict and design, in part due to limited data sets and the need for complex nonlinear simulations. This work presents a novel physics-enhanced machine learning (ML) and optimization framework tailored to address the challenges of designing intricate spinodal metamaterials with customized mechanical properties in large-deformation scenarios where computational modeling is restrictive and experimental data is sparse. By utilizing large-deformation experimental data directly, this approach facilitates the inverse design of spinodal structures with precise finite-strain mechanical responses. The framework sheds light on instability-induced pattern formation in spinodal metamaterials—observed experimentally and in selected nonlinear simulations—leveraging physics-based inductive biases in the form of nonconvex energetic potentials. Altogether, this combined ML, experimental, and computational effort provides a route for efficient and accurate design of complex spinodal metamaterials for large-deformation scenarios where energy absorption and prediction of nonlinear failure mechanisms is essential.</p>"
authors:
  - family-names: Thakolkaran
    given-names: Prakash
    orcid: "https://orcid.org/0000-0002-5836-6588"
  - family-names: Espinal
    given-names: Michael
    orcid: "https://orcid.org/0000-0003-1223-1431"
  - family-names: Dhulipala
    given-names: Somayajulu
    orcid: "https://orcid.org/0000-0002-3144-8583"
  - family-names: Kumar
    given-names: Siddhant
    orcid: "https://orcid.org/0000-0003-1602-8641"
  - family-names: Portela
    given-names: Carlos M.
    orcid: "https://orcid.org/0000-0002-2649-4235"
title: "Data and Code underlying publication: Experiment-informed finite-strain inverse design of spinodal metamaterials"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/bdc2b8d2-d000-4f56-ab0e-e64a147220fd.v1
license: MIT
date-released: 2024-12-30