arXiv subjects

Miguel A. L. Marques

Publications and source records attributed to Miguel A. L. Marques.

At least 19 recordsLinked to original sources

arXiv2026

Pressure-transferable universal machine-learning potentials: the WBM-MatPES benchmark and Tuga-FF

Universal machine-learning interatomic potentials hold the promise to replace costly ab initio calculations in materials science workflows. However, the accuracy and transferability of current models are limited by the availability of training data. We release WBM-MatPES, a dataset of 182 thousand structures recomputed with consistent MatPES settings, together with two reference convex hulls of different depths and test sets at six pressures up to 150 GPa. These datasets reproduce a realistic discovery workflow and allow potentials to be ranked systematically at this level of theory, similar to the Matbench Discovery leaderboard at the Materials Project settings. In this benchmark, five published MatPES potentials agree within 12 meV/atom at 25 GPa, yet their errors increase dramatically to 0.4-5 eV/atom at 150 GPa. To address this limitation, we develop Tuga-FF, a graph transformer trained on datasets spanning ambient and high-pressure configurations alike. Tuga-FF is the most accurate model in the benchmark at ambient pressure, with an energy error of 13.6 meV/atom, and maintains an error of just 21 meV/atom at 150 GPa. We believe this benchmark will encourage the community to train higher-accuracy models and generate richer datasets, advancing machine-learned interatomic potentials as a whole.

cond-mat.mtrl-sci↗

arXiv2026

How Thermodynamically Accessible are Quaternary Mixed-Metal Chalcohalides?

Quaternary mixed-metal chalcohalides (MMCHs) are a promising mixed-anion semiconductor family, but their thermodynamic accessibility remains largely unexplored. We combine density functional theory with the Alexandria Materials Database to carry out a rapid and reliable evaluation of the thermodynamic accessibility of 54 M(II)$_2$M(III)Ch$_2$X$_3$ compounds. All evaluated MMCHs are predicted to be thermodynamically unstable, but a considerable fraction of MMCHs lie within computational uncertainty of the convex hull. Their predicted decomposition follows five distinct reaction pathways, most commonly into M(III)$_2$Ch$_3$, M(II)X$_2$, and M(II)Ch. Solution processing of six Sn-based MMCHs supports our analysis. Notably, this includes the first synthesis of the previously unreported Sn$_2$InS$_2$Br$_3$ in a multiphase film. Our approach identifies convex hull proximity and competing phase formation as key constraints on MMCH synthetic accessibility during solution processing, thereby guiding future MMCH exploration.

cond-mat.mtrl-sci↗

arXiv2026

High-throughput computational discovery of CuI-based ternary $p$-type transparent conductors

P-type transparent conducting materials (TCMs) remain scarce, limiting progress in transparent electronics and tandem photovoltaics. Here, we perform a high-throughput computational search across CuI-based ternary X--Cu--I compounds using the minima hopping method combined with density functional theory. Filtering for thermodynamic stability, optical transparency, and light hole effective mass, we identify 58 candidate p-type TCMs, of which 49 are previously unreported. Twenty are p-type degenerate semiconductors with intrinsic hole carriers, including Cu$_2$FI$_2$ (calculated gap 3.75 eV, hole effective mass $0.262\,m_0$). We further use this dataset to revisit the chemical modulation of the valence band (CMVB) design rules, originally formulated for Cu-based delafossite oxides. Four generalizations emerge: linear Cu coordination is not required, as tetrahedral coordination (CN = 4) is compatible with p-type TCM character; the mechanism extends to halide-based systems with I $5p$ anion states; the Cu $d$ state energy center $ε_d$ is a quantitative descriptor for valence band dispersion; and Cu undercoordination in monovalent-anion hosts drives intrinsic p-type carrier generation through stoichiometric charge balance. These results establish a generalized computational framework for the design of p-type halide TCMs.

cond-mat.mtrl-sci↗

arXiv2026

High-throughput study of electrical conductivity in ordered metals

We present a computational framework that integrates machine learning with high-throughput ab initio calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with predicted high conductivities comparable to that of aluminum (36.59 x $10^6$ S/m). We perform full electron-phonon coupling calculations for the top-performing materials, yielding results in good agreement with available experimental data. Our analysis reveals that while the noble metals (Ag, Au, Cu) possess a conductivity that remains difficult to surpass due to their unique electronic structure and low scattering, compounds like LiBePt2 can achieve comparable performance by utilizing valence electrons from light elements to shift high-scattering d-states beneath the Fermi level. This study not only identifies novel high-performance conductors but also demonstrates the predictive power of combining statistical learning with detailed ab initio calculations.

cond-mat.mtrl-sci↗

arXiv2026

Quantum geometry and critical temperature enhancement in MgB$_2$ superconductivity

MgB$_2$, a phonon-mediated superconductor with record-high critical temperature $T_c\simeq 39$ K, is revisited to obtain a comprehensive theory of electrons, phonons, and their coupling with minimal ab initio input. We construct compact analytic models for the electronic structure, phonons, and electron-phonon coupling (EPC) of MgB$_2$. We show that strong in-plane B $sp^2$ bonding realizes an obstructed band structure whose natural description is a bond-centered kagome lattice, yielding small quasi-2D $σ$-band Fermi-surface cylinders and pronounced quantum-geometric effects. The phonon spectrum is found to closely track that of a graphene-like boron layer, but the heavy intercalated Mg atoms dominate the three acoustic branches and rigidly lift the boron modes into the optical sector, while the in-plane B-B bond-stretching mode exhibits a pronounced softening along $Γ$-A. By symmetry, this $Γ$-point bond-stretching mode is the only $Γ$ phonon that can couple to the $σ$ Fermi surface, explaining its dominant contribution to the EPC. Upon electron doping toward the doubly degenerate band edge of the $σ$ sheets, we find that a reduced density of states competes with enhanced EPC matrix elements. At light electron doping, ab initio calculations show that the EPC enhancement dominates, leading to an increase in $T_c$ (within the clean doping limit without disorder effects). Using the Gaussian approximation for the EPC tensor, we further show that this enhancement is overwhelmingly quantum geometric in origin, arising from a geometric EPC contribution of the small $σ$ Fermi surface peaked at $Γ$. Overall, our results provide a transparent, symmetry-based account of superconductivity in MgB$_2$ and suggest that quantum-geometric effects can be essential for shaping doping trends in phonon-mediated superconductors.

cond-mat.supr-con↗

arXiv2026

Machine Learning Materials Properties by Encoding Orbital-Projected Density of States

Graph neural networks have become the dominant machine-learning architecture for predicting materials properties from crystal structures. Yet the initialization of atomic node features has received comparatively little attention, and conventional approaches rely on static elemental descriptors that carry no information about the quantum-mechanical electronic environment of each atom in its crystalline host. Here we show that augmenting atomic node representations with site-projected orbital density of states (pDOS) fingerprints, computed directly from density functional theory calculations, yields systematic and substantial improvements in predictive performance.These representations are fused with Pettifor elemental embeddings at each atomic site before message passing. For the superconducting critical temperature $T_c$ and the optical dielectric constant $ε_{\infty}$,the pDOS augmentation reduces prediction errors by 22.9% and 27.9%, respectively, relative to the elemental-descriptor baseline. These improvements are comparable to those achieved by doubling the training-set size. The gains are, however, contingent on training-set size. For the magnetic exchange energies of Heusler compounds, a substantially smaller dataset, the improvement is reduced,indicating that pDOS augmentation is most effective when the training data exceeds the length of the pDOS feature vector. We introduce an interpretable spectral attention-gating mechanism that reveals that the model autonomously learns to prioritize the orbital channels and energy windows most physically relevant to each target property. These results establish pDOS-augmented graph nodes as a broadly applicable strategy for infusing first-principles electronic-structure knowledge into graph networks, opening a practical route to high-accuracy property prediction in data-scarce regimes.

cond-mat.mtrl-sci↗

arXiv2026

Semi-Local Exchange-Correlation Approximations in Density Functional Theory

Density functional theory has become the workhorse of modern electronic structure calculations, with wide-ranging applications in chemistry, physics, materials science, biochemistry, etc. At its heart lies the exchange-correlation functional, a quantity which exactly encapsulates the many-body effects stemming from the quantum mechanical interactions between the electrons. Yet, the exact functional is unknown, and computationally tractable approximations are therefore necessary for practical applications. Over the past six decades, hundreds of density functional approximations have been proposed with varying accuracy and computational efficiency. This review surveys the theoretical foundations of semi-local functionals, including local density approximations, generalized gradient approximations, and meta-generalized gradient approximations. We provide a comprehensive, consistently organized discussion that consolidates both historical developments and recent advances in this field. Beginning with the essentials of Kohn-Sham density functional theory, we present the construction principles of semi-local exchange-correlation functionals. Special attention is given to the physical motivations underlying functional development, the mathematical properties that guide their construction, and the practical considerations that determine their applicability across different chemical and physical systems. This work is intended to serve as both an introduction for newcomers to the field and a comprehensive reference for practitioners. By consolidating the extensive literature on semi-local functionals and providing a unified framework for their construction and application, we aim to facilitate further developments in density functional approximations and their use in tackling the diverse challenges of modern computational chemistry and condensed matter physics.

physics.chem-ph↗

arXiv2026

AI-Driven Expansion and Application of the Alexandria Database

We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.

cond-mat.mtrl-sci↗

arXiv2026

Ab-initio superfluid weight and superconducting penetration depth

Machine learning and high-throughput screening approaches to superconductor discovery require physically meaningful descriptors that capture essential physics while remaining computationally tractable. The superfluid weight is an ideal descriptor as it is a prerequisite for superconductivity, determines the magnetic penetration depth and the Berezinskii-Kosterlitz-Thouless transition temperature in two-dimensional materials, may limit the critical temperature in unconventional superconductors through phase coherence, and reveals quantum geometric contributions to supercurrent transport. We develop a computationally efficient framework for calculating the zero-temperature, mean-field superfluid weight for uniform pairing from density functional theory band structures and Bloch wavefunctions. We separately evaluate the conventional contribution from band curvature and the geometric contribution from quantum geometry. To validate the method, we calculate London penetration depths for a few conventional superconductors (Al, Pb, Nb, MgB$_2$, LuRu$_3$B$_2$ and YRu$_3$B$_2$) and find good agreement with experiment after accounting for nonlocal corrections, strong-coupling effects, and sample quality. The conventional contribution dominates by orders of magnitude in these wide-band materials, as expected. This framework provides a foundation for large-scale screening of superconducting candidates and exploring quantum geometric effects in unconventional superconductors.

cond-mat.supr-con↗

arXiv2026

MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy

Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that simultaneously span broad chemical diversity and provide reliable uncertainty estimates has limited their wider applicability. In this work, we introduce MACE4IRmol, an uncertainty-aware foundation model ensemble built on the MACE architecture. MACE4IRmol is trained on ~16 million molecular geometries and the corresponding density-functional theory (DFT) energies, forces, and dipole moments from the QCML dataset. The training data encompasses approximately 80 elements and a diverse set of molecules, including organic and inorganic compounds, and metal complexes. Importantly, MACE4IRmol is formulated as an ensemble of models to enable uncertainty quantification, which helps improve robustness in chemically diverse systems. Within this ensemble, separate models are trained with and without explicit dispersion corrections, allowing systematic assessment of van der Waals effects. In addition, MACE4IRmol delivers accurate predictions of energies, forces, dipole moments, and infrared spectra at a fraction of the computational cost of DFT, while enabling the explicit inclusion of nuclear quantum effects in infrared spectrum simulations. By combining generality, accuracy, efficiency, and uncertainty estimation, MACE4IRmol opens the door to rapid and reliable infrared spectra prediction for complex and diverse molecular systems.

physics.chem-ph↗

arXiv2026

Machine Learning-Guided Discovery of Kagome Superconductors YRu3B2 and LuRu3B2

We report the experimental discovery of bulk superconductivity in two kagome lattice compounds, YRu$_3$B$_2$ and LuRu$_3$B$_2$, which were predicted through machine learning-accelerated high-throughput screening combined with first principles calculations. These materials crystallize in the hexagonal CeCo$_3$B$_2$-type structure with planar kagome networks formed by Ru atoms. We observe superconducting critical temperatures of $T_{c} = 0.81$~K for YRu$_3$B$_2$ and $T_{c} = 0.95$~K for LuRu$_3$B$_2$, confirmed through magnetization and specific heat measurements. Both compounds exhibit nearly 100\% superconducting volume fractions, demonstrating bulk superconductivity. Compared with LaRu$_3$Si$_2$, YRu$_3$B$_2$ and LuRu$_3$B$_2$ show a more dispersive Ru local $d_{x^2-y^2}$ quasi-flat band (and thus a reduced DOS at $E_F$) together with an overall hardening of the phonon spectrum, both of which lower the electron-phonon coupling (EPC) constant $λ$. Meanwhile, the dominant real-space EPC between Ru local $d_{x^2-y^2}$ states and the low-frequency Ru in-plane local $x$ branch remains nearly unchanged, indicating that the reduction of $λ$ originates from the $d_{x^2-y^2}$ DOS reduction and the overall phonon hardening. Superfluid weight calculations show that conventional contributions dominate over quantum geometric effects due to the dispersive nature of bands near the Fermi level. This work demonstrates the effectiveness of integrating machine learning screening, first principles theory, and experimental synthesis for accelerating the discovery of new superconducting materials.

cond-mat.supr-con↗

arXiv2025

High-Tc superconductivity above 130 K in cubic MH4 compounds at ambient pressure

Hydrides have long been considered promising candidates for achieving room-temperature superconductivity; however, the extremely high pressures typically required for high critical temperatures remain a major challenge in experiment. Here, we propose a class of high-Tc ambient-pressure superconductors with MH4 stoichiometry. These hydrogen-based compounds adopt the bcc PtHg4 structure type, in which hydrogen atoms occupy the one-quarter body-diagonal sites of metal lattices, with the metal atoms acting as chemical templates for hydrogen assembly. Through comprehensive first-principles calculations, we identify three promising superconductors, PtH4, AuH4 and PdH4, with superconducting critical temperatures of 84 K, 89 K, and 133 K, respectively, all surpassing the liquid-nitrogen temperature threshold of 77 K. The remarkable superconducting properties originate from strong electron-phonon coupling associated with hydrogen vibrations, which in turn arise from phonon softening in the mid-frequency range. Our results provide crucial insights into the design of high-Tc superconductors suitable for future experiments and applications at ambient pressure.

cond-mat.supr-con↗

arXiv2025

Accelerating point defect photo-emission calculations with machine learning interatomic potentials

We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spectra of atomic or molecular emitters with ab initio accuracy. By replacing the costly density functional theory (DFT) computation of phonon modes with much faster MLIP phonon mode calculations, our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss. We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states. The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface. Across all the systems, we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes. This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity, opening pathways to high-throughput screening of defect-engineered materials. Our work not only demonstrates accelerated calculation of PL spectra with DFT accuracy, but also makes such calculations tractable for more complex materials.

cond-mat.mtrl-sci↗

arXiv2025

Generative AI for Crystal Structures: A Review

As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architecture, representation, conditioning, and materials domain to categorize the diverse range of current generative AI models. We discuss data sources and address challenges related to performance metrics, emphasizing the need for standardized benchmarks. Specific examples and applications of novel generated structures are presented. Finally, we examine current limitations and future directions in this rapidly evolving field, highlighting its potential to accelerate the discovery of new inorganic materials.

cond-mat.mtrl-sci↗

arXiv2025

Universal Machine Learning Potentials under Pressure

Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable performance across diverse applications. However, critical blind spots in their reliability persist. Here, we address one such significant gap by systematically investigating the accuracy of uMLIPs under extreme pressure conditions from 0 to 150 GPa. Our benchmark reveals that while these models excel at standard pressure, their predictive accuracy deteriorates considerably as pressure increases. This decline in performance originates from fundamental limitations in the training data rather than in algorithmic constraints. In fact, we show that through targeted fine-tuning on high-pressure configurations, the robustness of the models can be easily increased. These findings underscore the importance of identifying and addressing overlooked regimes in the development of the next generation of truly universal interatomic potentials.

cond-mat.mtrl-sci↗

arXiv2025

Universal Machine Learning Potential for Systems with Reduced Dimensionality

We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc.), one- (nanowires, nanoribbons, nanotubes, etc.), two- (atomic layers and slabs) and three-dimensional (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for three-dimensional systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01-0.02 angstrom and errors in the energy below 10~meV/atom across all dimensionalities. These results demonstrate that state-of-the-art universal machine learning interatomic potentials have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces.

cond-mat.mtrl-sci↗

arXiv2025

Prediction of high-Tc superconductivity in ternary actinium beryllium hydrides at low pressure

Hydrogen-rich superconductors are promising candidates to achieve room-temperature superconductivity. However, the extreme pressures needed to stabilize these structures significantly limit their practical applications. An effective strategy to reduce the external pressure is to add a light element M that binds with H to form MHx units, acting as a chemical precompressor. We exemplify this idea by performing ab initio calculations of the Ac-Be-H phase diagram, proving that the metallization pressure of Ac-H binaries, for which critical temperatures as high as 200 K were predicted at 200 GPa, can be significantly reduced via beryllium incorporation. We identify three thermodynamically stable (AcBe2H10, AcBeH8, and AcBe2H14) and four metastable compounds (fcc AcBeH8, AcBeH10, AcBeH12 and AcBe2H16). All of them are superconductors. In particular, fcc AcBeH8 remains dynamically stable down to 10 GPa, where it exhibits a superconducting transition temperature Tc of 181 K. The Be-H bonds are responsible for the exceptional properties of these ternary compounds and allow them to remain dynamically stable close to ambient pressure. Our results suggest that high-Tc superconductivity in hydrides is achievable at low pressure and may stimulate experimental synthesis of ternary hydrides.

cond-mat.supr-con↗

arXiv2025

Universal Machine Learning Interatomic Potentials are Ready for Phonons

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 for predictingenergy, forces, and stresses, combining innovative architectures with big data. Here, we benchmarkthese models on their ability to predict harmonic phonon properties, which are critical for under-standing the vibrational and thermal behavior of materials. Using around 10 000 ab initio phononcalculations, we evaluate model performance across various phonon-related parameters to test theuniversal applicability of these models. The results reveal that some models achieve high accuracyin predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamicalequilibrium. These findings highlight the importance of considering phonon-related properties inthe development of universal machine learning interatomic potentials.

cond-mat.mtrl-sci↗