MM
Miguel A. L. Marques
cond-mat.mtrl-scicond-mat.supr-conphysics.comp-phphysics.chem-phcond-mat.str-elquant-phcond-mat.othercond-mat.mes-hallphysics.atm-clusphysics.atom-ph
On Valency
published · living versionsW_mb7ftr6x·v1 · currentpublished
Density-based mixing parameter for hybrid functionals
with Julien Vidal, Micael J. T. Oliveira, Lucia Reining, Silvana Botti
1 version
W_ybfbmdu6·v1published
Preprints & journals
175 papers in the corpus · 2001–2026Linear response for machine-learned interatomic potentials: phonons at arbitrary wave vectors from the primitive cell2610.23545v1 · Thalis H. B. da Silva, Hai-Chen Wang, Miguel A. L. Marques2026 · 0 citationsarXiv
Pressure-transferable universal machine-learning potentials: the WBM-MatPES benchmark and Tuga-FF2610.14891v1 · Pierre-Paul De Breuck, Thalis H. B. da Silva, Antoine Loew et al.2026 · 0 citationsarXiv
How Thermodynamically Accessible are Quaternary Mixed-Metal Chalcohalides?2609.38125v1 · Pascal Henkel, G. Krishnamurthy Grandhi, Jarno Laakso et al.2026 · 0 citationsarXiv
High-throughput computational discovery of CuI-based ternary -type transparent conductors2609.31277v1 · Michael Seifert, Miguel A. L. Marques, Silvana Botti2026 · 0 citationsarXiv
Quantum geometry and critical temperature enhancement in MgB superconductivity2607.19458v1 · Yi Jiang, Haoyu Hu, Dumitru Calugaru et al.2026 · 0 citationsarXiv
Machine Learning Materials Properties by Encoding Orbital-Projected Density of States2607.07339v1 · Paulo Pires, Pierre-Paul De Breuck, Mauro Fava et al.2026 · 0 citationsarXiv
Strong band bowing in BiOX (X = Cl, Br, I) due to halogen alloying.42300477 · Wang, Hai-Chen, Vives, Aude, Marques, Miguel A L et al.2026 · 0 citationsPhysical chemistry chemical physics : PCCP. 2026;28(25):15567-15576
Ab-initio superfluid weight and superconducting penetration depth2603.10955v1 · Kaja H. Hiorth, Martin Gutierrez-Amigo, Th'eo Cavignac et al.2026 · 0 citationsarXiv
Semi-Local Exchange-Correlation Approximations in Density Functional Theory2602.17333v2 · Fabien Tran, Susi Lehtola, Stefano Pittalis et al.2026 · 0 citationsarXiv
System-conditioned reparameterization of the SCAN functional for accurate bandgaps: from analytical constraints to machine learning.42021921 · Dovale-Farelo, Viviana, Tavadze, Pedram, Marques, Miguel A L et al.2026 · 0 citationsnpj computational materials. 2026;12(1):162
Machine Learning-Guided Discovery of Kagome Superconductors YRu3B2 and LuRu3B22512.16945v2 · Rose Albu Mustaf, Sajilesh K. P., Sanu Mishra et al.2025 · 0 citationsarXiv
AI-Driven Expansion and Application of the Alexandria Database2512.09169v2 · Th'eo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck et al.2025 · 5 citationsarXiv
High-Tc superconductivity above 130 K in cubic MH4 compounds at ambient pressure2511.04222v1 · Xinxin Li, Weishuo Xu, Zengguang Zhou et al.2025 · 2 citationsarXiv
The maximum Tc of conventional superconductors at ambient pressure.40931021 · Gao, Kun, Cerqueira, Tiago F T, Sanna, Antonio et al.2025 · 26 citationsNature communications. 2025;16(1):8253
Generative AI for Crystal Structures: A Review2509.02723v1 · Pierre-Paul De Breuck, Hai-Chen Wang, Gian-Marco Rignanese et al.2025 · 23 citationsarXiv
MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy2508.19118v2 · Nitik Bhatia, Ondrej Krejci, Silvana Botti et al.2025 · 2 citationsarXiv
Universal Machine Learning Potentials under Pressure2508.17792v1 · Antoine Loew, Jonathan Schmidt, Silvana Botti et al.2025 · 1 citationarXiv
Universal Machine Learning Potential for Systems with Reduced Dimensionality2508.15614v1 · Giulio Benedini, Antoine Loew, Matti Hellstrom et al.2025 · 1 citationarXiv
Enhanced Superconductivity in X4H15 Compounds via Hole-Doping at Ambient Pressure.40714910 · Gao, Kun, Cui, Wenwen, Cerqueira, Tiago F T et al.2025 · 3 citationsAdvanced science (Weinheim, Baden-Wurttemberg, Germany). 2025;12(39):e08419
Machine-learning accelerated prediction of two-dimensional conventional superconductors.39937617 · da Silva, Thalis H B, Cavignac, Théo, Cerqueira, Tiago F T et al.2026 · 10 citationsMaterials horizons. 2025;12(10):3408-3419
Accelerating point defect photo-emission calculations with machine learning interatomic potentials2505.01403v2 · Kartikeya Sharma, Antoine Loew, Haiyuan Wang et al.2025 · 11 citationsarXiv
Theory of Superconductivity in LaRuSi and Predictions of New Kagome Flat Band Superconductors2503.20867v1 · Junze Deng, Yi Jiang, Tiago F. T. Cerqueira et al.2025 · 1 citationarXiv
High-throughput study of kagome compounds in the AV3Sb5 family2503.17124v1 · Thalis H. B. da Silva, Tiago F. T. Cerqueira, Hai-Chen Wang et al.2025 · 0 citationsarXiv
A generative material transformer using Wyckoff representation2501.16051v2 · Pierre-Paul De Breuck, Hashim A. Piracha, Gian-Marco Rignanese et al.2025 · 9 citationsarXiv
Universal Machine Learning Interatomic Potentials are Ready for Phonons2412.16551v2 · Antoine Loew, Dewen Sun, Hai-Chen Wang et al.2024 · 68 citationsarXiv
Assessing Exchange-Correlation Functionals for Accurate Densities of Solids.39626866 · Aouina, Ayoub, Borlido, Pedro, Marques, Miguel A L et al.2025 · 6 citationsJournal of chemical theory and computation. 2024;20(24):10852-10860
Prediction of high-Tc superconductivity in ternary actinium beryllium hydrides at low pressure2411.19028v2 · Kun Gao, Wenwen Cui, Jingming Shi et al.2024 · 48 citationsPhysical Review B 109,014501(2024)
Ambient pressure high temperature superconductivity in RbPH facilitated by ionic anharmonicity2411.03822v1 · \DJor\dje Dangi'c, Yue-Wen Fang, Tiago F. T. Cerqueira et al.2024 · 11 citationsarXiv
In-architecture X-ray assisted C-Br dissociation for on-surface fabrication of diamondoid chains2410.19466v1 · Yan Wang, Niklas Grabicki, Hibiki Orio et al.2024 · 0 citationsarXiv
Exploring flat-band properties in two-dimensional M3QX7 compounds.39082370 · Wang, Hai-Chen, Rauch, Tomáš, Tellez-Mora, Andres et al.2024 · 4 citationsPhysical chemistry chemical physics : PCCP. 2024;26(32):21558-21567
A non-orthogonal representation of the chemical space2406.19761v2 · Tiago F. T. Cerqueira, Haichen Wang, Silvana Botti et al.2024 · 0 citationsarXiv
Ruddlesden-Popper Oxyfluorides La2Ni1-xCuxO3F2 (0 ≤ x ≤ 1): Impact of the Ni/Cu Ratio on the Thermal Stability and Magnetic Properties.38829674 · Jacobs, Jonas, Wang, Hai-Chen, Marques, Miguel A L et al.2024 · 4 citationsInorganic chemistry. 2024;63(24):11317-11324
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange.39118978 · Evans, Matthew L, Bergsma, Johan, Merkys, Andrius et al.2024 · 53 citationsDigital discovery. 2024;3(8):1509-1533
Searching Materials Space for Hydride Superconductors at Ambient Pressure2403.13496v1 · Tiago F. T. Cerqueira, Yue-Wen Fang, Ion Errea et al.2024 · 86 citationsarXiv
Ruddlesden-Popper Oxyfluorides La2Ni1-xCuxO3F2 (0 ≤ x ≤ 1): Impact of the Ni/Cu Ratio on the Structure.38506110 · Jacobs, Jonas, Wang, Hai-Chen, Marques, Miguel A L et al.2024 · 9 citationsInorganic chemistry. 2024;63(13):6075-6081
Sampling the Materials Space for Conventional Superconducting Compounds.37985412 · Cerqueira, Tiago F T, Sanna, Antonio, Marques, Miguel A L2024 · 63 citationsAdvanced materials (Deerfield Beach, Fla.). 2024;36(1):e2307085
Two-Dimensional Noble Metal Chalcogenides in the Frustrated Snub-Square Lattice.37905788 · Wang, Hai-Chen, Huran, Ahmad W, Marques, Miguel A L et al.2023 · 1 citationThe journal of physical chemistry letters. 2023;14(44):9969-9977
Prediction of Ambient Pressure Conventional Superconductivity above 80K in Thermodynamically Stable Hydride Compounds2310.06804v1 · Antonio Sanna, Tiago F. T. Cerqueira, Yue-Wen Fang et al.2023 · 111 citationsnpj Comput Mater 10, 44 (2024)
Reproducibility of density functional approximations: How new functionals should be reported.37725491 · Lehtola, Susi, Marques, Miguel A L2023 · 24 citationsThe Journal of chemical physics. 2023;159(11)
Sampling the Whole Materials Space for Conventional Superconducting Materials2307.10728v1 · Tiago F. T. Cerqueira, Antonio Sanna, Miguel A. L. Marques2023 · 0 citationsarXiv
Searching for ductile superconducting Heusler X2YZ compounds2306.04439v1 · Noah Hoffmann, Tiago F. T. Cerqueira, Pedro Borlido et al.2023 · 21 citationsarXiv
Structure prediction and characterization of CuI-based ternary -type transparent conductors2305.14941v1 · Michael Seifert, Tom'as Rauch, Miguel A. L. Marques et al.2023 · 0 citationsarXiv
Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials.36949007 · Schmidt, Jonathan, Hoffmann, Noah, Wang, Hai-Chen et al.2023 · 110 citationsAdvanced materials (Deerfield Beach, Fla.). 2023;35(22):e2210788
Transfer learning on large datasets for the accurate prediction of material properties2303.03000v1 · Noah Hoffmann, Jonathan Schmidt, Silvana Botti et al.2023 · 37 citationsarXiv
Effects of hole doping on the electronic and optical properties of transparent conducting copper iodide2212.10855v1 · Michael Seifert, Miguel A. L. Marques, Silvana Botti2022 · 0 citationsarXiv
Symmetry-based computational search for novel binary and ternary 2D materials2212.03975v2 · Hai-Chen Wang, Jonathan Schmidt, Miguel A.L. Marques et al.2022 · 33 citationsarXiv
Many recent density functionals are numerically ill-behaved.36347696 · Lehtola, Susi, Marques, Miguel A L2022 · 43 citationsThe Journal of chemical physics. 2022;157(17):174114
Homogeneous Electron Liquid in Arbitrary Dimensions: Exchange and Correlation Using the Singwi-Tosi-Land-Sjölander Approach2210.03024v3 · L. V. Duc Pham, Pascal Sattler, Miguel A. L. Marques et al.2022 · 1 citationNew J. Phys. 25, 083040 (2023)
Large-scale machine-learning-assisted exploration of the whole materials space2210.00579v1 · Jonathan Schmidt, Noah Hoffmann, Hai-Chen Wang et al.2022 · 10 citationsarXiv
Efficient and improved prediction of the band offsets at semiconductor heterojunctions from meta-GGA density functionals: A benchmark study.36182416 · Ghosh, Arghya, Jana, Subrata, Rauch, Tomáš et al.2022 · 30 citationsThe Journal of chemical physics. 2022;157(12):124108
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