4 papers
Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires
Pedro H. M. Zanineli, Bruno Focassio, Gabriel R. Schleder
Foundation machine-learning interatomic potentials (MLIPs) enable atomistic simulations at substantially lower computational cost than first-principles methods, but their reliabili…
Heterogeneous Molecular Signatures of Human Odor Perception
P. Zanineli, E. V. C. Lopes, G. R. Schleder +3
Understanding how molecular structure gives rise to odor perception remains a long-standing challenge, with ongoing debate over whether olfaction is primarily governed by molecular…
Fuzzy Neural Network Performance and Interpretability of Quantum Wavefunction Probability Predictions
Pedro H. M. Zanineli, Matheus Zaia Monteiro, Vinicius Francisco Wasques +2
Predicting quantum wavefunction probability distributions is crucial for computational chemistry and materials science, yet machine learning (ML) models often face a trade-off betw…
Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
Bruno Focassio, Luis Paulo Mezzina Freitas, Gabriel R. Schleder
Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency…