arXiv subjects

Frederico P. Delgado

Publications and source records attributed to Frederico P. Delgado.

5 recordsLinked to original sources

arXiv2026

Hamiltonian learning reveals optoelectronic mechanisms across thermodynamic state space in soft semiconductors

Predicting optoelectronic response across thermodynamic state space requires coupling finite-temperature nuclear dynamics to electronic structure at scales where direct first-principles calculations are impractical. Machine-learning force fields and Hamiltonian-learning models provide scalable predictions, but integrating them into reliable and interpretable workflows remains challenging. Here, we introduce FLOW-OTTER, a modular, model-agnostic framework that automates molecular dynamics, Hamiltonian prediction, observable extraction, reliability assessment, and Hamiltonian-level interpretation. We demonstrate FLOW-OTTER in halide perovskites, soft semiconductors whose anharmonic fluctuations strongly modulate electronic response. Using FLOW-OTTER, we show that independently learned nuclear and electronic models remain predictive when composed end-to-end, reproducing experimental temperature- and pressure-dependent band-gap trends using models trained only on first-principles targets at zero pressure. By resolving the nonlinear evolution of Pb-$s$/Br-$p$ antibonding at the valence-band maximum, it identifies the microscopic origin of the asymmetric pressure response. FLOW-OTTER thus establishes Hamiltonian learning as a general route from thermodynamic trajectories to experimentally grounded optoelectronic mechanisms.

cond-mat.mtrl-sci↗

arXiv2026

Interplay between Electronic Structure, Chemical Bonding, and Lattice Symmetry in Bismuth Vanadate

Bismuth vanadate (BiVO$_4$) is a prototypical oxide photocatalyst that occurs in both tetragonal and monoclinic scheelite phases with markedly different photocatalytic and photoelectrochemical activities. Accurately identifying the monoclinic phase as the ground state and explaining the origin of its symmetry-breaking distortion are unusually challenging from a theoretical perspective, with various levels of theory and associated physical interpretations for this behaviour reported in the literature. Here, we resolve these discrepancies by systematically assessing the role of exact exchange with and without spin-orbit coupling, demonstrating that an accurate treatment of electronic localization is essential to stabilize the monoclinic scheelite structure. Using this framework, we compute the electronic band structure through dense sampling of the Brillouin zone and show that the band edges in monoclinic and tetragonal BiVO$_4$ lie far from conventional high-symmetry paths, leading to substantial differences in band gaps and carrier effective masses. Choosing the exchange-correlation functional that best reproduces the crystal structure leads to excellent predictions of the band gap once excitonic and thermal effects are taken into account. In addition, we show that the monoclinic distortion is driven by charge transfer between non-equivalent oxygen sites, which breaks the lattice symmetry and is suppressed by self-interaction errors when using semi-local DFT. These results establish a direct connection between the exchange-correlation functional, electronic localization, chemical bonding, and structural stability in BiVO$_4$, providing a foundation for robust ab initio descriptions of phase stability and optoelectronic properties in such complex oxides.

cond-mat.mtrl-sci↗

arXiv2026

Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction

Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. Physics-inspired approximate models offer greater data efficiency and intuitive understanding, but often sacrifice accuracy and transferability. Here we present HAMSTER, a physics-informed machine learning framework for predicting the quantum-mechanical Hamiltonian of complex chemical systems. Starting from an approximate model encoding essential physical effects, HAMSTER captures the critical influence of dynamic environments on Hamiltonians using only few explicit first-principles calculations. We demonstrate our approach on halide perovskites, achieving accurate prediction of optoelectronic properties across temperature and compositional variations, and scalability to systems containing tens of thousands of atoms. This work highlights the power of physics-informed Hamiltonian learning for accurate and interpretable optoelectronic property prediction in large, complex systems.

cond-mat.mtrl-sci↗

arXiv2025

Ultrafast light-induced formation of a metastable hidden state in bismuth vanadate

Bismuth vanadate (BiVO$_4$) is a key photocatalyst for solar fuel applications, yet fundamental questions remain regarding the nature of photogenerated polaronic states and the lattice dynamics that govern its light-to-chemical pathways. Here, we use femtosecond optical pump-X-ray probe measurements to track the photoinduced electronic and structural dynamics in BiVO$_4$ across multiple length and time scales. Transient X-ray absorption spectroscopy captures sub-picosecond electron localization within VO$_4$ tetrahedra, consistent with small polaron formation, whereas time-resolved X-ray diffraction reveals a slower, multi-picosecond lattice reorganization into a hidden photoexcited state that is structurally distinct from both the monoclinic ground state and the high-temperature tetragonal phase. Supported by density functional theory, we show that hole-lattice interactions dynamically reduce the ground state monoclinic distortion, stabilizing the hidden state. Our results demonstrate that electron- and hole-lattice coupling jointly shape the excited state landscape, with implications for carrier transport, interfacial energetics, and light-to-chemical energy conversion pathways.

cond-mat.mtrl-sci↗

arXiv2025

Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces

The spectacular performance of halide perovskites in optoelectronic devices is rooted in their tolerance to defects. Previous studies showed that defects in these materials generate shallow electronic states. However, how these shallow states persist amid the pronounced atomic dynamics on halide perovskite surfaces remains unknown. This work reveals that electronic states at surfaces of prototypical CsPbBr$_3$ are energetically distributed at room temperature akin to well-passivated inorganic semiconductors, even when covalent bonds remain cleaved and undercoordinated. Specifically, a striking tendency for shallow surface states is found with approximately 70% of surface-state energies appearing within 0.2 eV or ${\approx}8k_\text{B}T$ from the valence-band edge. While these findings do not rule out occurrence of deep traps per se, they show that even when surface states appear deeper in the gap, they are not energetically isolated and are less likely to act as traps. We achieve this result by accelerating first-principles calculations via machine learning and show that the unique atomic dynamics in these materials render the formation of deep electronic states at their surfaces unlikely. These findings reveal the microscopic mechanism behind the low density of deep states at dynamic halide perovskite surfaces, which is key to their device performance.

cond-mat.mtrl-sci↗