collaborators

6 papers

cond-mat.mtrl-sci2026

CARBON-2D Topological Descriptor (C2DTD): An Interpretable and Physics-Informed Representation for Two-Dimensional Carbon Networks

Felipe Hawthorne, Marcelo Lopes Pereira Junior, Fabiano Manoel de Andrade +2

Two-dimensional (2D) carbon networks, from pristine graphene to defect-rich and amorphous monolayers, exhibit a complex structure-energy landscape governed not only by local bondin…

cond-mat.mtrl-sci2026

Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings

Felipe Hawthorne, Leandro Seixas, James M. Almeida +2

The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior r…

cond-mat.mtrl-sci2025

Hexa-Graphyne: A Transparent and Semimetallic 2D Carbon Allotrope with Distinct Optical Properties

Jhionathan de Lima, Cristiano Francisco Woellner

Herein, we conduct a comprehensive investigation of Hexa-graphyne (HXGY), a planar carbon allotrope formed by distorted hexagonal and rectangular rings incorporating sp and sp-…

cond-mat.soft2025

Role of Translational Noise in Motility-Induced Phase Separation of Hard Active Particles

Felipe Hawthorne, Pablo de Castro, José A. Freire

Self-propelled particles, like motile cells and artificial colloids, can spontaneously form macroscopic clusters. This phenomenon is called motility-induced phase separation (MIPS)…

cond-mat.mtrl-sci2025

Melanin-Based Compounds as Low-Cost Sensors for Nitroaromatics: Theoretical Insights on Molecular Interactions and Optoelectronic Responses

Jo{ã}o Paulo Cachaneski-Lopes, Felipe Hawthorne, Cristiano Woellner +5

Nitroaromatic compounds (NACs) are used in various industrial applications including dyes, inks, herbicides, pharmaceuticals, and explosives. Due to their toxicity and environmenta…

cond-mat.mtrl-sci2025

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties

Felipe Hawthorne, Paulo R. E. Raulino, Ronaldo Rodrigues Pelá +1

Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between…