INCT Materials Informatics Meeting 2025
02/07/2025 and 03/07/2025
Venue
The event will be held at the new Ilum living center, on the main campus of the Brazilian Center for Research in Energy and Materials (CNPEM). The CNPEM is located in Campinas, São Paulo State in Brazil. CNPEM is a renowned research center, which houses the Ilum School of Science and four national laboratories specializing in areas such as synchrotron light, biosciences, nanotechnology, and biorenewables, making it a premier destination for cutting-edge scientific research and development.
Organizing committee
Adalberto Fazzio (Ilum/CNPEM).
James Moraes de Almeida (Ilum/CNPEM).
Agenda
Talk 1 – Fazzio & Dalpian
Oppening
Talk 2- João Nuno Barbosa Rodrigues
Discovering new materials – a conversation between experiments, high-throughput calculations and machine learning methods
Talk 3 – Ramon Cardias
Magnetization Control in Fe3GeTe2 and Fe3GaTe2 Nanoribbons via Spin-Orbit Torques.
Talk 4 – Bruno Focassio
Applying AI to Nanotechnology: The Simulation and Artificial Intelligence Lab at LNNano
Talk 5 – Amauri Jardim de Paula
Using LLMs in pipelines for the analysis of the material science literature
Talk 6 – Antonio Augusto Alves Junior
INCT – Materials Informatics Database
Talk 7 – Augusto de Lelis Araújo
Twisted Homo- and heterobilayers: A high-throughput DFT database
Talk 8 – Marcelo Albuquerque
Less is more: how choosing data properly influences machine learning-based outcomes?
Talk 9 – Lídia Carvalho Gomes
Defeitos em semicondutores para energias renováveis: Explorando a fase pi do SnS
Talk 10 – Alberto Torres Riera Junior
Machine Learning Potential for the Structural properties of Hematite
Tutorial 1 – George Trenins, Hannah Bertschi, Jorge Castro
Simulating nuclear quantum effects with path integral methods
Tutorial 2 – Mariana Rossi, Zekun Lou, Paolo Lazzaroni
Machine learning methods in atomistic simulations: basics and predicting the electronic structure
Tutorial 3 – Krystof Brezina, Elia Stocco, Shubham Sharma
Machine learning interatomic potentials – active learning techniques