28 citations · 52 across the 17 of their papers we have counts for
8 papers · 1 filter
Fine-Tuned Machine-Learned Interatomic Potentials for Structural and Vibrational Properties of Twisted 2D Materials
Viet-Anh Tran, Viet-Hung Nguyen, Wei Chen +2
Twisted van der Waals bilayers form moiré superlattices whose structural and vibrational properties are highly sensitive to variations in local stacking registry and the degree of…
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
Anand Babu, Rogério Almeida Gouvêa, Gian-Marco Rignanese
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…
optimade-maker: Automated generation of interoperable materials APIs from static datasets
Kristjan Eimre, Matthew L. Evans, Bud Macaulay +5
Atomistic structural data are central to materials science, condensed matter physics, and chemistry, and are increasingly digitised across diverse repositories and databases. Inter…
Machine Learning for Electrode Materials: Property Prediction via Composition
Hao Wu, Cameron Hargreaves, Arpit Mishra +1
In this work, we benchmark three leading composition based Machine Learning (ML) frameworks, MODNet, CrabNet, and a random forest model based on Magpie features, predicting the pro…
A critical assessment of bonding descriptors for predicting materials properties
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen +4
Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuabl…
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…
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…