6 papers
Unveiling and quantifying the topology-dependent pre-melting of nanoparticles
Marthe Bideault, Arnaud Allera, Ryoji Asahi +2
The melting of metallic nanoparticles is governed by surface premelting, a phenomenon traditionally modeled as the isotropic growth of a uniform liquid shell. Challenging this clas…
Hierarchical Stacking Optimization Using Dirichlet's Process (SoDip): Towards Accelerated Design for Graft Polymerization
Amgad Ahmed Ali Ibrahim, Hein Htet, Ryoji Asahi
Radiation-induced grafting (RIG) enables precise functionalization of polymer films for ion-exchange membranes, CO2-separation membranes, and battery electrolytes by generating rad…
Extracting ORR Catalyst Information for Fuel Cell from Scientific Literature
Hein Htet, Amgad Ahmed Ali Ibrahim, Yutaka Sasaki +1
The oxygen reduction reaction (ORR) catalyst plays a critical role in enhancing fuel cell efficiency, making it a key focus in material science research. However, extracting struct…
Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials
Alex Kutana, Koki Yoshimochi, Ryoji Asahi
Atomistic simulations of properties of materials at finite temperatures are computationally demanding and require models that are more efficient than the ab initio approaches. Mach…
Polyvalent Machine-Learned Potential for Cobalt: from Bulk to Nanoparticles
Marthe Bideault, Jérôme Creuze, Ryoji Asahi +1
We present the development and applications of a quadratic Spectral Neighbor Analysis Potential (q-SNAP) for ferromagnetic cobalt. Trained on Density Functional Theory calculations…
Representing Born effective charges with equivariant graph convolutional neural networks
Alex Kutana, Koji Shimizu, Satoshi Watanabe +1
Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-…