5 papers
From Symmetry to Stability: Structural and Electronic Transformation in CsKInI
Mohammad Bakhsh, Victor Trinquet, Rogério Almeida Gouvêa +2
CsKInI is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations…
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
Anand Babu, Rogério Almeida Gouvêa, Rogério Almeida Gouvêa +1
Inverse materials design is shifting materials discovery from forward prediction toward targeted proposal of candidates that satisfy objectives under physical constraints. Here, we…
VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
Rogério Almeida Gouvêa, Gian-Marco Rignanese
While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is…
MEIDNet: Multimodal generative AI framework for inverse materials design
Anand Babu, Rogério Almeida Gouvêa, Pierre Vandergheynst +1
In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastiv…
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
Rogério Almeida Gouvêa, Pierre-Paul De Breuck, Tatiane Pretto +2
This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent…