9 papers
Floralens: a Deep Learning Model for the Portuguese Native Flora
António Filgueiras, Eduardo R. B. Marques, LuÃs M. B. Lopes +2
Machine-learning techniques, especially deep convolutional neural networks, are pivotal for image-based identification of biological species in many Citizen Science platforms. In t…
MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
Nitik Bhatia, Ondrej Krejci, Silvana Botti +2
Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that…
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
Kartikeya Sharma, Antoine Loew, Haiyuan Wang +4
We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spe…
Universal Machine Learning Potentials under Pressure
Antoine Loew, Jonathan Schmidt, Silvana Botti +1
Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable per…
Universal Machine Learning Potential for Systems with Reduced Dimensionality
Giulio Benedini, Antoine Loew, Matti Hellstrom +2
We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically,…
Prediction of high-Tc superconductivity in ternary actinium beryllium hydrides at low pressure
Kun Gao, Wenwen Cui, Jingming Shi +5
Hydrogen-rich superconductors are promising candidates to achieve room-temperature superconductivity. However, the extreme pressures needed to stabilize these structures significan…