Miguel A. L. Marques

Centre National de la Recherche Scientifique — France

Physics and Astronomy · Physical Sciences

67h-index24.6kcitations417works8.22yr avg
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Research Topics

Advanced Chemical Physics Studies(85), Machine Learning in Materials Science(77), High-pressure geophysics and materials(36), Spectroscopy and Quantum Chemical Studies(32), Boron and Carbon Nanomaterials Research(27)

Publications417 total

Machine Learning-Guided Discovery of Kagome Superconductors YRu3B2 and LuRu3B2
arXiv (Cornell University)·2025Open Access
Superconductivity in YRu3B2 and LuRu3B2
arXiv (Cornell University)·2025Open Access
AI-Driven Expansion and Application of the Alexandria Database
arXiv (Cornell University)·2025Open Access
Generative AI for crystal structures: a review
npj Computational Materials·2025Open Access
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
npj Computational Materials·2025· 4 citedOpen Access
High-Tc superconductivity above 130 K in cubic MH4 compounds at ambient pressure
ArXiv.org·2025· 1 citedOpen Access
Author response for "Universal machine learning potentials under pressure"
2025
Ambient pressure high temperature superconductivity in RbPH3 facilitated by ionic anharmonicity
Computational Materials Today·2025· 2 citedOpen Access
In-Architecture X-ray Assisted C–Br Dissociation for On-Surface Fabrication of Nanodiamond Chains
ACS Applied Nano Materials·2025· 2 cited
The maximum Tc of conventional superconductors at ambient pressure
Nature Communications·2025· 10 citedOpen Access
Neuro-Spectral Architectures for Causal Physics-Informed Networks
arXiv (Cornell University)·2025Open Access
Generative AI for Crystal Structures: A Review
arXiv (Cornell University)·2025Open Access
Universal Machine Learning Potential for Systems with Reduced Dimensionality
arXiv (Cornell University)·2025Open Access
Enhanced Superconductivity in X <sub>4</sub> H <sub>15</sub> Compounds via Hole‐Doping at Ambient Pressure
Advanced Science·2025Open Access
Author response for "High-throughput study of kagome compounds in the AV&lt;sub&gt;3&lt;/sub&gt;Sb&lt;sub&gt;5&lt;/sub&gt; family"
2025
Author response for "High-throughput study of kagome compounds in the AV&lt;sub&gt;3&lt;/sub&gt;Sb&lt;sub&gt;5&lt;/sub&gt; family"
2025
Universal machine learning interatomic potentials are ready for phonons
npj Computational Materials·2025· 26 citedOpen Access
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
arXiv (Cornell University)·2025Open Access
Theory of Superconductivity in LaRu$_3$Si$_2$ and Predictions of New Kagome Flat Band Superconductors
ArXiv.org·2025· 1 citedOpen Access
High-throughput study of kagome compounds in the AV3Sb5 family
arXiv (Cornell University)·2025Open Access
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Frequent Co-authors

Silvana Botti(18), Ángel Rubio(14), E. K. U. Gross(12), Alberto Castro(8), Micael J. T. Oliveira(8), Jonathan Schmidt(6), Xavier Andrade(5), Martin Lüders(5), Nektarios N. Lathiotakis(5), Fernando Nogueira(4), Andrea Floris(4), G. Profeta(4), A. Continenza(4), S. Massidda(4), Fabien Bruneval(3), N. Helbig(3), David A. Strubbe(3), Sabine Körbel(3), Maximilian Amsler(3), José A. Flores‐Livas(3)