47 citations · 54 across the 10 of their papers we have counts for
11 papers
High-throughput computational discovery of CuI-based ternary -type transparent conductors
Michael Seifert, Miguel A. L. Marques, Silvana Botti
P-type transparent conducting materials (TCMs) remain scarce, limiting progress in transparent electronics and tandem photovoltaics. Here, we perform a high-throughput computationa…
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,…
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…
Enhanced superconductivity in X4H15compounds via hole-doping at ambient pressure
Kun Gao, Wenwen Cui, Tiago F. T. Cerqueira +3
This study presents a computational investigation of X4H15 compounds (where X represents a metal) as potential superconductors at ambient conditions or under pressure. Through syst…
Universal Machine Learning Interatomic Potentials are Ready for Phonons
Antoine Loew, Dewen Sun, Hai-Chen Wang +2
There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models…
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…
A non-orthogonal representation of the chemical space
Tiago F. T. Cerqueira, Haichen Wang, Silvana Botti +1
We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application…
Floralens: a Deep Learning Model for the Portuguese Native Flora
António Filgueiras, Eduardo R. B. Marques, Luís 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…
Structure prediction and characterization of CuI-based ternary -type transparent conductors
Michael Seifert, Tomáš Rauch, Miguel A. L. Marques +1
Zincblende copper iodide has attracted significant interest as a potential material for transparent electronics, thanks to its exceptional light transmission capabilities in the vi…